diff --git a/.github/workflows/python.yml b/.github/workflows/python.yml
index 30e6272fb31b..713168545fb5 100644
--- a/.github/workflows/python.yml
+++ b/.github/workflows/python.yml
@@ -67,7 +67,7 @@ jobs:
         name:
           - conda-python-docs
           - conda-python-3.12-nopandas
-          - conda-python-3.11-pandas-1.5.2
+          - conda-python-3.11-pandas-2.0.3
           - conda-python-3.14-pandas-latest
           - conda-python-3.13-no-numpy
         include:
@@ -79,11 +79,11 @@ jobs:
             image: conda-python
             title: AMD64 Conda Python 3.12 Without Pandas
             python: "3.12"
-          - name: conda-python-3.11-pandas-1.5.2
+          - name: conda-python-3.11-pandas-2.0.3
             image: conda-python-pandas
-            title: AMD64 Conda Python 3.11 Pandas 1.5.2
+            title: AMD64 Conda Python 3.11 Pandas 2.0.3
             python: "3.11"
-            pandas: "1.5.2"
+            pandas: "2.0.3"
             numpy: "1.23.2"
           - name: conda-python-3.14-pandas-latest
             image: conda-python-pandas
diff --git a/dev/tasks/tasks.yml b/dev/tasks/tasks.yml
index c443c5a7e7c7..89d239a0a266 100644
--- a/dev/tasks/tasks.yml
+++ b/dev/tasks/tasks.yml
@@ -727,7 +727,7 @@ tasks:
 
   ############################## Integration tests ############################
 
-{% for python_version, pandas_version, numpy_version, cache_leaf in [("3.11", "1.5.2", "1.23.2", True),
+{% for python_version, pandas_version, numpy_version, cache_leaf in [("3.11", "2.0.3", "1.23.2", True),
                                                                      ("3.12", "latest", "latest", False),
                                                                      ("3.13", "latest", "1.26.2", False),
                                                                      ("3.13", "latest", "latest", False),
diff --git a/docs/source/python/install.rst b/docs/source/python/install.rst
index 5fa9ab6c778b..d076ca96436f 100644
--- a/docs/source/python/install.rst
+++ b/docs/source/python/install.rst
@@ -74,7 +74,7 @@ Dependencies
 Optional dependencies
 
 * **NumPy 1.23.2** or higher.
-* **pandas 1.5.2** or higher,
+* **pandas 2.0.3** or higher,
 * **cffi**.
 
 Additional packages PyArrow is compatible with are :ref:`fsspec <filesystem-fsspec>`
diff --git a/python/pyarrow/array.pxi b/python/pyarrow/array.pxi
index 5fc74969abfd..e259a5e4fbaf 100644
--- a/python/pyarrow/array.pxi
+++ b/python/pyarrow/array.pxi
@@ -2383,10 +2383,6 @@ cdef _array_like_to_pandas(obj, options, types_mapper):
         arr = dtype.__from_arrow__(obj)
         return pandas_api.series(arr, name=name, copy=False)
 
-    if pandas_api.is_v1():
-        # ARROW-3789: Coerce date/timestamp types to datetime64[ns]
-        c_options.coerce_temporal_nanoseconds = True
-
     if isinstance(obj, Array):
         with nogil:
             check_status(ConvertArrayToPandas(c_options,
diff --git a/python/pyarrow/pandas-shim.pxi b/python/pyarrow/pandas-shim.pxi
index 3dbb97cd8028..94802da6b060 100644
--- a/python/pyarrow/pandas-shim.pxi
+++ b/python/pyarrow/pandas-shim.pxi
@@ -38,7 +38,7 @@ cdef class _PandasAPIShim(object):
         object _array_like_types, _is_extension_array_dtype, _lock
         bint has_sparse
         bint _pd024
-        bint _is_v1, _is_ge_v21, _is_ge_v23, _is_ge_v3, _is_ge_v3_strict
+        bint _is_ge_v21, _is_ge_v23, _is_ge_v3, _is_ge_v3_strict
 
     def __init__(self):
         self._lock = Lock()
@@ -61,25 +61,23 @@ cdef class _PandasAPIShim(object):
         self._pd = pd
         self._version = pd.__version__
         self._loose_version = Version(pd.__version__)
-        self._is_v1 = False
 
-        if self._loose_version < Version('1.0.0'):
+        if self._loose_version < Version('2.0.3'):
             self._have_pandas = False
             if raise_:
                 raise ImportError(
-                    f"pyarrow requires pandas 1.0.0 or above, pandas {self._version} is "
+                    f"pyarrow requires pandas 2.0.3 or above, pandas {self._version} is "
                     "installed"
                 )
             else:
                 warnings.warn(
-                    f"pyarrow requires pandas 1.0.0 or above, pandas {self._version} is "
+                    f"pyarrow requires pandas 2.0.3 or above, pandas {self._version} is "
                     "installed. Therefore, pandas-specific integration is not "
                     "used.",
                     stacklevel=2
                 )
                 return
 
-        self._is_v1 = self._loose_version < Version('2.0.0')
         self._is_ge_v21 = self._loose_version >= Version('2.1.0')
         self._is_ge_v23 = self._loose_version >= Version('2.3.0.dev0')
         self._is_ge_v3 = self._loose_version >= Version('3.0.0.dev0')
@@ -166,10 +164,6 @@ cdef class _PandasAPIShim(object):
         self._check_import()
         return self._version
 
-    def is_v1(self):
-        self._check_import()
-        return self._is_v1
-
     def is_ge_v21(self):
         self._check_import()
         return self._is_ge_v21
diff --git a/python/pyarrow/pandas_compat.py b/python/pyarrow/pandas_compat.py
index d8fd383d31d1..ccb89fc05da5 100644
--- a/python/pyarrow/pandas_compat.py
+++ b/python/pyarrow/pandas_compat.py
@@ -790,8 +790,6 @@ def _reconstruct_block(item, columns=None, extension_columns=None, return_block=
 
 
 def make_datetimetz(unit, tz):
-    if _pandas_api.is_v1():
-        unit = 'ns'  # ARROW-3789: Coerce date/timestamp types to datetime64[ns]
     tz = pa.lib.string_to_tzinfo(tz, prefer_zoneinfo=_pandas_api.is_ge_v3())
     return _pandas_api.datetimetz_type(unit, tz=tz)
 
diff --git a/python/pyarrow/src/arrow/python/arrow_to_pandas.cc b/python/pyarrow/src/arrow/python/arrow_to_pandas.cc
index f163266f3b87..348d352a0482 100644
--- a/python/pyarrow/src/arrow/python/arrow_to_pandas.cc
+++ b/python/pyarrow/src/arrow/python/arrow_to_pandas.cc
@@ -1293,7 +1293,7 @@ struct ObjectWriterVisitor {
                               PyObject** out) {
       ARROW_DCHECK(internal::BorrowPandasDataOffsetType() != nullptr);
       // DateOffset objects do not add nanoseconds component to pd.Timestamp.
-      // as of  Pandas 1.3.3
+      // as of Pandas 1.3.3
       // (https://github.com/pandas-dev/pandas/issues/43892).
       // So convert microseconds and remainder to preserve data
       // but give users more expected results.
diff --git a/python/pyarrow/table.pxi b/python/pyarrow/table.pxi
index fc7c4fcfc84f..17ca35e476d8 100644
--- a/python/pyarrow/table.pxi
+++ b/python/pyarrow/table.pxi
@@ -4070,10 +4070,6 @@ def table_to_blocks(options, Table table, categories, extension_columns):
         c_options.extension_columns = make_shared[unordered_set[c_string]](
             unordered_set[c_string]({tobytes(col) for col in extension_columns}))
 
-    if pandas_api.is_v1():
-        # ARROW-3789: Coerce date/timestamp types to datetime64[ns]
-        c_options.coerce_temporal_nanoseconds = True
-
     if c_options.self_destruct:
         # Move the shared_ptr, table is now unsafe to use further
         c_table = move(table.sp_table)
diff --git a/python/pyarrow/tests/interchange/test_conversion.py b/python/pyarrow/tests/interchange/test_conversion.py
index 81713b94e6db..cc105178ad87 100644
--- a/python/pyarrow/tests/interchange/test_conversion.py
+++ b/python/pyarrow/tests/interchange/test_conversion.py
@@ -17,7 +17,6 @@
 
 from datetime import datetime as dt
 import pyarrow as pa
-from pyarrow.vendored.version import Version
 import pytest
 
 try:
@@ -122,9 +121,6 @@ def test_offset_of_sliced_array():
     ]
 )
 def test_pandas_roundtrip(uint, int, float, np_float_str):
-    if Version(pd.__version__) < Version("1.5.0"):
-        pytest.skip("__dataframe__ added to pandas in 1.5.0")
-
     arr = [1, 2, 3]
     table = pa.table(
         {
@@ -153,9 +149,6 @@ def test_pandas_roundtrip(uint, int, float, np_float_str):
 @pytest.mark.pandas
 def test_pandas_roundtrip_string():
     # See https://github.com/pandas-dev/pandas/issues/50554
-    if Version(pd.__version__) < Version("1.6"):
-        pytest.skip("Column.size() bug in pandas")
-
     arr = ["a", "", "c"]
     table = pa.table({"a": pa.array(arr)})
 
@@ -182,9 +175,6 @@ def test_pandas_roundtrip_string():
 @pytest.mark.pandas
 def test_pandas_roundtrip_large_string():
     # See https://github.com/pandas-dev/pandas/issues/50554
-    if Version(pd.__version__) < Version("1.6"):
-        pytest.skip("Column.size() bug in pandas")
-
     arr = ["a", "", "c"]
     table = pa.table({"a_large": pa.array(arr, type=pa.large_string())})
 
@@ -192,36 +182,25 @@ def test_pandas_roundtrip_large_string():
         from_dataframe as pandas_from_dataframe
     )
 
-    if Version(pd.__version__) >= Version("2.0.1"):
-        pandas_df = pandas_from_dataframe(table)
-        result = pi.from_dataframe(pandas_df)
-
-        assert result["a_large"].to_pylist() == table["a_large"].to_pylist()
-        assert pa.types.is_large_string(table["a_large"].type)
-        assert pa.types.is_large_string(result["a_large"].type)
+    pandas_df = pandas_from_dataframe(table)
+    result = pi.from_dataframe(pandas_df)
 
-        table_protocol = table.__dataframe__()
-        result_protocol = result.__dataframe__()
+    assert result["a_large"].to_pylist() == table["a_large"].to_pylist()
+    assert pa.types.is_large_string(table["a_large"].type)
+    assert pa.types.is_large_string(result["a_large"].type)
 
-        assert table_protocol.num_columns() == result_protocol.num_columns()
-        assert table_protocol.num_rows() == result_protocol.num_rows()
-        assert table_protocol.num_chunks() == result_protocol.num_chunks()
-        assert table_protocol.column_names() == result_protocol.column_names()
+    table_protocol = table.__dataframe__()
+    result_protocol = result.__dataframe__()
 
-    else:
-        # large string not supported by pandas implementation for
-        # older versions of pandas
-        # https://github.com/pandas-dev/pandas/issues/52795
-        with pytest.raises(AssertionError):
-            pandas_from_dataframe(table)
+    assert table_protocol.num_columns() == result_protocol.num_columns()
+    assert table_protocol.num_rows() == result_protocol.num_rows()
+    assert table_protocol.num_chunks() == result_protocol.num_chunks()
+    assert table_protocol.column_names() == result_protocol.column_names()
 
 
 @pytest.mark.pandas
 def test_pandas_roundtrip_string_with_missing():
     # See https://github.com/pandas-dev/pandas/issues/50554
-    if Version(pd.__version__) < Version("1.6"):
-        pytest.skip("Column.size() bug in pandas")
-
     arr = ["a", "", "c", None]
     table = pa.table({"a": pa.array(arr),
                       "a_large": pa.array(arr, type=pa.large_string())})
@@ -230,29 +209,20 @@ def test_pandas_roundtrip_string_with_missing():
         from_dataframe as pandas_from_dataframe
     )
 
-    if Version(pd.__version__) >= Version("2.0.2"):
-        pandas_df = pandas_from_dataframe(table)
-        result = pi.from_dataframe(pandas_df)
+    pandas_df = pandas_from_dataframe(table)
+    result = pi.from_dataframe(pandas_df)
 
-        assert result["a"].to_pylist() == table["a"].to_pylist()
-        assert pa.types.is_string(table["a"].type)
-        assert pa.types.is_large_string(result["a"].type)
+    assert result["a"].to_pylist() == table["a"].to_pylist()
+    assert pa.types.is_string(table["a"].type)
+    assert pa.types.is_large_string(result["a"].type)
 
-        assert result["a_large"].to_pylist() == table["a_large"].to_pylist()
-        assert pa.types.is_large_string(table["a_large"].type)
-        assert pa.types.is_large_string(result["a_large"].type)
-    else:
-        # older versions of pandas do not have bitmask support
-        # https://github.com/pandas-dev/pandas/issues/49888
-        with pytest.raises(NotImplementedError):
-            pandas_from_dataframe(table)
+    assert result["a_large"].to_pylist() == table["a_large"].to_pylist()
+    assert pa.types.is_large_string(table["a_large"].type)
+    assert pa.types.is_large_string(result["a_large"].type)
 
 
 @pytest.mark.pandas
 def test_pandas_roundtrip_categorical():
-    if Version(pd.__version__) < Version("2.0.2"):
-        pytest.skip("Bitmasks not supported in pandas interchange implementation")
-
     arr = ["Mon", "Tue", "Mon", "Wed", "Mon", "Thu", "Fri", "Sat", None]
     table = pa.table(
         {"weekday": pa.array(arr).dictionary_encode()}
@@ -299,8 +269,6 @@ def test_pandas_roundtrip_categorical():
 @pytest.mark.pandas
 @pytest.mark.parametrize("unit", ['s', 'ms', 'us', 'ns'])
 def test_pandas_roundtrip_datetime(unit):
-    if Version(pd.__version__) < Version("1.5.0"):
-        pytest.skip("__dataframe__ added to pandas in 1.5.0")
     from datetime import datetime as dt
 
     # timezones not included as they are not yet supported in
@@ -308,12 +276,7 @@ def test_pandas_roundtrip_datetime(unit):
     dt_arr = [dt(2007, 7, 13), dt(2007, 7, 14), dt(2007, 7, 15)]
     table = pa.table({"a": pa.array(dt_arr, type=pa.timestamp(unit))})
 
-    if Version(pd.__version__) < Version("1.6"):
-        # pandas < 2.0 always creates datetime64 in "ns"
-        # resolution
-        expected = pa.table({"a": pa.array(dt_arr, type=pa.timestamp('ns'))})
-    else:
-        expected = table
+    expected = table
 
     from pandas.api.interchange import (
         from_dataframe as pandas_from_dataframe
@@ -337,9 +300,6 @@ def test_pandas_roundtrip_datetime(unit):
     "np_float_str", ["float32", "float64"]
 )
 def test_pandas_to_pyarrow_with_missing(np_float_str):
-    if Version(pd.__version__) < Version("1.5.0"):
-        pytest.skip("__dataframe__ added to pandas in 1.5.0")
-
     np_array = np.array([0, np.nan, 2], dtype=np.dtype(np_float_str))
     datetime_array = [None, dt(2007, 7, 14), dt(2007, 7, 15)]
     df = pd.DataFrame({
@@ -359,9 +319,6 @@ def test_pandas_to_pyarrow_with_missing(np_float_str):
 
 @pytest.mark.pandas
 def test_pandas_to_pyarrow_float16_with_missing():
-    if Version(pd.__version__) < Version("1.5.0"):
-        pytest.skip("__dataframe__ added to pandas in 1.5.0")
-
     # np.float16 errors if ps.is_nan is used
     # pyarrow.lib.ArrowNotImplementedError: Function 'is_nan' has no kernel
     # matching input types (halffloat)
@@ -482,9 +439,6 @@ def test_nan_as_null():
 
 @pytest.mark.pandas
 def test_allow_copy_false():
-    if Version(pd.__version__) < Version("1.5.0"):
-        pytest.skip("__dataframe__ added to pandas in 1.5.0")
-
     # Test that an error is raised when a copy is needed
     # to create a bitmask
 
@@ -501,9 +455,6 @@ def test_allow_copy_false():
 
 @pytest.mark.pandas
 def test_allow_copy_false_bool_categorical():
-    if Version(pd.__version__) < Version("1.5.0"):
-        pytest.skip("__dataframe__ added to pandas in 1.5.0")
-
     # Test that an error is raised for boolean
     # and categorical dtype (copy is always made)
 
diff --git a/python/pyarrow/tests/parquet/test_pandas.py b/python/pyarrow/tests/parquet/test_pandas.py
index 53864ff15ea2..8dbd5d71b9bc 100644
--- a/python/pyarrow/tests/parquet/test_pandas.py
+++ b/python/pyarrow/tests/parquet/test_pandas.py
@@ -524,9 +524,6 @@ def test_pandas_categorical_roundtrip():
 @pytest.mark.pandas
 def test_categories_with_string_pyarrow_dtype(tempdir):
     # gh-33727: writing to parquet should not fail
-    if Version(pd.__version__) < Version("1.3.0"):
-        pytest.skip("PyArrow backed string data type introduced in pandas 1.3.0")
-
     df1 = pd.DataFrame({"x": ["foo", "bar", "foo"]}, dtype="string[pyarrow]")
     df1 = df1.astype("category")
 
diff --git a/python/pyarrow/tests/test_compute.py b/python/pyarrow/tests/test_compute.py
index 39167e76c8f6..1e08e73668e5 100644
--- a/python/pyarrow/tests/test_compute.py
+++ b/python/pyarrow/tests/test_compute.py
@@ -2612,8 +2612,6 @@ def test_strftime():
 
 
 def _check_datetime_components(timestamps, timezone=None):
-    from pyarrow.vendored.version import Version
-
     ts = pd.to_datetime(timestamps).tz_localize(
         "UTC").tz_convert(timezone).to_series()
     tsa = pa.array(ts, pa.timestamp("ns", tz=timezone))
@@ -2626,17 +2624,11 @@ def _check_datetime_components(timestamps, timezone=None):
         pa.field('iso_day_of_week', pa.int64())
     ]
 
-    if Version(pd.__version__) < Version("1.1.0"):
-        # https://github.com/pandas-dev/pandas/issues/33206
-        iso_year = ts.map(lambda x: x.isocalendar()[0]).astype("int64")
-        iso_week = ts.map(lambda x: x.isocalendar()[1]).astype("int64")
-        iso_day = ts.map(lambda x: x.isocalendar()[2]).astype("int64")
-    else:
-        # Casting is required because pandas isocalendar returns int32
-        # while arrow isocalendar returns int64.
-        iso_year = ts.dt.isocalendar()["year"].astype("int64")
-        iso_week = ts.dt.isocalendar()["week"].astype("int64")
-        iso_day = ts.dt.isocalendar()["day"].astype("int64")
+    # Casting is required because pandas isocalendar returns int32
+    # while arrow isocalendar returns int64.
+    iso_year = ts.dt.isocalendar()["year"].astype("int64")
+    iso_week = ts.dt.isocalendar()["week"].astype("int64")
+    iso_day = ts.dt.isocalendar()["day"].astype("int64")
 
     iso_calendar = pa.StructArray.from_arrays(
         [iso_year, iso_week, iso_day],
diff --git a/python/pyarrow/tests/test_pandas.py b/python/pyarrow/tests/test_pandas.py
index c42dc04cd196..2a782de1648c 100644
--- a/python/pyarrow/tests/test_pandas.py
+++ b/python/pyarrow/tests/test_pandas.py
@@ -488,14 +488,10 @@ def test_string_column_index(self):
 
     @pytest.mark.parametrize('unit', ['us', 'ns'])
     def test_datetimetz_column_index(self, unit):
-        ext_kwargs = {}
-        if Version(pd.__version__) >= Version("2.0.0"):
-            # unit argument not supported on date_range for pandas < 2.0.0
-            ext_kwargs = {'unit': unit}
         df = pd.DataFrame(
             [(1, 'a', 2.0), (2, 'b', 3.0), (3, 'c', 4.0)],
             columns=pd.date_range(
-                start='2017-01-01', periods=3, tz='America/New_York', **ext_kwargs
+                start='2017-01-01', periods=3, tz='America/New_York', unit=unit
             )
         )
         t = pa.Table.from_pandas(df, preserve_index=True)
@@ -504,10 +500,7 @@ def test_datetimetz_column_index(self, unit):
         column_indexes, = js['column_indexes']
         assert column_indexes['name'] is None
         assert column_indexes['pandas_type'] == 'datetimetz'
-        if ext_kwargs:
-            assert column_indexes['numpy_type'] == f'datetime64[{unit}]'
-        else:
-            assert column_indexes['numpy_type'] == 'datetime64[ns]'
+        assert column_indexes['numpy_type'] == f'datetime64[{unit}]'
 
         md = column_indexes['metadata']
         assert md['timezone'] == 'America/New_York'
@@ -751,12 +744,8 @@ def test_mismatch_metadata_schema(self):
         # It is possible that the metadata and actual schema is not fully
         # matching (eg no timezone information for tz-aware column)
         # -> to_pandas() conversion should not fail on that
-        ext_kwargs = {}
-        if Version(pd.__version__) >= Version("2.0.0"):
-            # unit argument not supported on date_range for pandas < 2.0.0
-            ext_kwargs = {'unit': 'ns'}
         df = pd.DataFrame({"datetime": pd.date_range(
-            "2020-01-01", periods=3, **ext_kwargs)})
+            "2020-01-01", periods=3, unit='ns')})
 
         # OPTION 1: casting after conversion
         table = pa.Table.from_pandas(df)
@@ -1156,8 +1145,6 @@ def test_timestamps_notimezone_nulls(self):
 
     @pytest.mark.parametrize('unit', ['s', 'ms', 'us', 'ns'])
     def test_timestamps_with_timezone(self, unit):
-        if Version(pd.__version__) < Version("2.0.0") and unit != 'ns':
-            pytest.skip("pandas < 2.0 only supports nanosecond datetime64")
         df = pd.DataFrame({
             'datetime64': np.array([
                 '2007-07-13T01:23:34.123',
@@ -1347,10 +1334,6 @@ def test_array_types_date_as_object(self, coerce_to_ns, expected_dtype):
         expected_days = np.array(['2000-01-01', None, '1970-01-01',
                                   '2040-02-26'], dtype='datetime64[D]')
 
-        if Version(pd.__version__) < Version("2.0.0"):
-            # ARROW-3789: Coerce date/timestamp types to datetime64[ns]
-            expected_dtype = 'datetime64[ns]'
-
         expected = np.array(['2000-01-01', None, '1970-01-01',
                              '2040-02-26'], dtype=expected_dtype)
 
@@ -1646,19 +1629,6 @@ def test_numpy_datetime64_columns(self):
             dtype='datetime64[s]')
         _check_array_from_pandas_roundtrip(datetime64_s)
 
-    def test_timestamp_to_pandas_coerces_to_ns(self):
-        # non-ns timestamp gets cast to ns on conversion to pandas
-        if Version(pd.__version__) >= Version("2.0.0"):
-            pytest.skip("pandas >= 2.0 supports non-nanosecond datetime64")
-
-        arr = pa.array([1, 2, 3], pa.timestamp('ms'))
-        expected = pd.Series(pd.to_datetime([1, 2, 3], unit='ms'))
-        s = arr.to_pandas()
-        tm.assert_series_equal(s, expected)
-        arr = pa.chunked_array([arr])
-        s = arr.to_pandas()
-        tm.assert_series_equal(s, expected)
-
     def test_timestamp_to_pandas_out_of_bounds(self):
         # ARROW-7758 check for out of bounds timestamps for non-ns timestamps
         # that end up getting coerced into ns timestamps.
@@ -1735,8 +1705,6 @@ def test_fixed_offset_timezone(self):
 
     @pytest.mark.parametrize("unit", ['s', 'ms', 'us', 'ns'])
     def test_timedeltas_no_nulls(self, unit):
-        if Version(pd.__version__) < Version("2.0.0"):
-            unit = 'ns'
         df = pd.DataFrame({
             'timedelta64': np.array([0, 3600000000000, 7200000000000],
                                     dtype=f'timedelta64[{unit}]')
@@ -1750,8 +1718,6 @@ def test_timedeltas_no_nulls(self, unit):
 
     @pytest.mark.parametrize("unit", ['s', 'ms', 'us', 'ns'])
     def test_timedeltas_nulls(self, unit):
-        if Version(pd.__version__) < Version("2.0.0"):
-            unit = 'ns'
         df = pd.DataFrame({
             'timedelta64': np.array([0, None, 7200000000000],
                                     dtype=f'timedelta64[{unit}]')
@@ -2419,11 +2385,6 @@ def test_to_list_of_structs_pandas(self):
             tm.assert_series_equal(series, expected)
 
     def test_to_list_of_maps_pandas(self):
-        if ((Version(np.__version__) >= Version("1.25.0.dev0")) and
-                (Version(pd.__version__) < Version("2.0.0"))):
-            # TODO: regression in pandas with numpy 1.25dev
-            # https://github.com/pandas-dev/pandas/issues/50360
-            pytest.skip("Regression in pandas with numpy 1.25")
         data = [
             [[('foo', ['a', 'b']), ('bar', ['c', 'd'])]],
             [[('baz', []), ('qux', None), ('quux', [None, 'e'])], [('quz', ['f', 'g'])]]
@@ -2443,13 +2404,6 @@ def test_to_list_of_maps_pandas_sliced(self):
         """
         A slightly more rigorous test for chunk/slice combinations
         """
-
-        if ((Version(np.__version__) >= Version("1.25.0.dev0")) and
-                (Version(pd.__version__) < Version("2.0.0"))):
-            # TODO: regression in pandas with numpy 1.25dev
-            # https://github.com/pandas-dev/pandas/issues/50360
-            pytest.skip("Regression in pandas with numpy 1.25")
-
         keys = pa.array(['ignore', 'foo', 'bar', 'baz',
                          'qux', 'quux', 'ignore']).slice(1, 5)
         items = pa.array(
@@ -3799,9 +3753,7 @@ def test_table_from_pandas_schema_field_order_metadata():
 
     result = table.to_pandas()
     coerce_cols_to_types = {"float": "float32"}
-    if Version(pd.__version__) >= Version("2.0.0"):
-        # Pandas v2 now support non-nanosecond time units
-        coerce_cols_to_types["datetime"] = "datetime64[s, UTC]"
+    coerce_cols_to_types["datetime"] = "datetime64[s, UTC]"
     expected = df[["float", "datetime"]].astype(coerce_cols_to_types)
 
     tm.assert_frame_equal(result, expected)
@@ -3969,10 +3921,7 @@ def test_to_pandas_split_blocks():
 
 
 def _get_mgr(df):
-    if Version(pd.__version__) < Version("1.1.0"):
-        return df._data
-    else:
-        return df._mgr
+    return df._mgr
 
 
 def _check_blocks_created(t, number):
@@ -4383,9 +4332,6 @@ def test_dictionary_from_pandas_specified_type():
 
 def test_convert_categories_to_array_with_string_pyarrow_dtype():
     # gh-33727: categories should be converted to pa.Array
-    if Version(pd.__version__) < Version("1.3.0"):
-        pytest.skip("PyArrow backed string data type introduced in pandas 1.3.0")
-
     df = pd.DataFrame({"x": ["foo", "bar", "foo"]}, dtype="string[pyarrow]")
     df = df.astype("category")
     indices = pa.array(df['x'].cat.codes)
@@ -4524,12 +4470,8 @@ def test_convert_to_extension_array(monkeypatch):
     tm.assert_frame_equal(result, df2)
 
     # monkeypatch pandas Int64Dtype to *not* have the protocol method
-    if Version(pd.__version__) < Version("1.3.0.dev"):
-        monkeypatch.delattr(
-            pd.core.arrays.integer._IntegerDtype, "__from_arrow__")
-    else:
-        monkeypatch.delattr(
-            pd.core.arrays.integer.NumericDtype, "__from_arrow__")
+    monkeypatch.delattr(
+        pd.core.arrays.integer.NumericDtype, "__from_arrow__")
     # Int64Dtype has no __from_arrow__ -> use normal conversion
     result = table.to_pandas()
     assert len(_get_mgr(result).blocks) == 1
@@ -4569,12 +4511,8 @@ def test_conversion_extensiontype_to_extensionarray(monkeypatch):
 
     # monkeypatch pandas Int64Dtype to *not* have the protocol method
     # (remove the version added above and the actual version for recent pandas)
-    if Version(pd.__version__) < Version("1.3.0.dev"):
-        monkeypatch.delattr(
-            pd.core.arrays.integer._IntegerDtype, "__from_arrow__")
-    else:
-        monkeypatch.delattr(
-            pd.core.arrays.integer.NumericDtype, "__from_arrow__")
+    monkeypatch.delattr(
+        pd.core.arrays.integer.NumericDtype, "__from_arrow__")
 
     result = arr.to_pandas()
     assert _get_mgr(result).blocks[0].values.dtype == np.dtype("int64")
@@ -4611,9 +4549,6 @@ def test_to_pandas_extension_dtypes_mapping():
 
 
 def test_to_pandas_extension_dtypes_mapping_complex_type():
-    # https://github.com/apache/arrow/pull/44720
-    if Version(pd.__version__) < Version("1.5.2"):
-        pytest.skip("Test relies on pd.ArrowDtype")
     pa_type = pa.struct(
         [
             pa.field("bar", pa.bool_(), nullable=False),
@@ -4636,9 +4571,6 @@ def test_to_pandas_extension_dtypes_mapping_complex_type():
 
 
 def test_array_to_pandas():
-    if Version(pd.__version__) < Version("1.1"):
-        pytest.skip("ExtensionDtype to_pandas method missing")
-
     for arr in [pd.period_range("2012-01-01", periods=3, freq="D").array,
                 pd.interval_range(1, 4).array]:
         result = pa.array(arr).to_pandas()
@@ -4653,20 +4585,11 @@ def test_array_to_pandas():
 def test_roundtrip_empty_table_with_extension_dtype_index():
     df = pd.DataFrame(index=pd.interval_range(start=0, end=3))
     table = pa.table(df)
-    if Version(pd.__version__) > Version("1.0"):
-        tm.assert_index_equal(table.to_pandas().index, df.index)
-    else:
-        tm.assert_index_equal(table.to_pandas().index,
-                              pd.Index([{'left': 0, 'right': 1},
-                                        {'left': 1, 'right': 2},
-                                        {'left': 2, 'right': 3}],
-                                       dtype='object'))
+    tm.assert_index_equal(table.to_pandas().index, df.index)
 
 
 @pytest.mark.parametrize("index", ["a", ["a", "b"]])
 def test_to_pandas_types_mapper_index(index):
-    if Version(pd.__version__) < Version("1.5.0"):
-        pytest.skip("ArrowDtype missing")
     df = pd.DataFrame(
         {
             "a": [1, 2],
@@ -4683,9 +4606,6 @@ def test_to_pandas_types_mapper_index(index):
 
 def test_array_to_pandas_types_mapper():
     # https://issues.apache.org/jira/browse/ARROW-9664
-    if Version(pd.__version__) < Version("1.2.0"):
-        pytest.skip("Float64Dtype extension dtype missing")
-
     data = pa.array([1, 2, 3], pa.int64())
 
     # Test with mapper function
@@ -4707,9 +4627,6 @@ def test_array_to_pandas_types_mapper():
 @pytest.mark.pandas
 def test_chunked_array_to_pandas_types_mapper():
     # https://issues.apache.org/jira/browse/ARROW-9664
-    if Version(pd.__version__) < Version("1.2.0"):
-        pytest.skip("Float64Dtype extension dtype missing")
-
     data = pa.chunked_array([pa.array([1, 2, 3], pa.int64())])
     assert isinstance(data, pa.ChunkedArray)
 
diff --git a/python/pyarrow/tests/test_schema.py b/python/pyarrow/tests/test_schema.py
index 029e14ca1628..db44ad32114b 100644
--- a/python/pyarrow/tests/test_schema.py
+++ b/python/pyarrow/tests/test_schema.py
@@ -27,12 +27,6 @@
 import pyarrow as pa
 
 import pyarrow.tests.util as test_util
-from pyarrow.vendored.version import Version
-
-try:
-    import pandas as pd
-except ImportError:
-    pass
 
 
 def test_schema_constructor_errors():
@@ -55,8 +49,6 @@ def test_type_integers():
 @pytest.mark.pandas
 def test_type_to_pandas_dtype():
     M8 = np.dtype('datetime64[ms]')
-    if Version(pd.__version__) < Version("2.0.0"):
-        M8 = np.dtype('datetime64[ns]')
     cases = [
         (pa.null(), np.object_),
         (pa.bool_(), np.bool_),
diff --git a/python/pyarrow/types.pxi b/python/pyarrow/types.pxi
index ec1a5a2ba9a3..8eea34224acc 100644
--- a/python/pyarrow/types.pxi
+++ b/python/pyarrow/types.pxi
@@ -186,9 +186,7 @@ def _get_pandas_tz_type(arrow_type, coerce_to_ns=False):
 
 
 def _to_pandas_dtype(arrow_type, options=None):
-    coerce_to_ns = (options and options.get('coerce_temporal_nanoseconds', False)) or (
-        _pandas_api.is_v1() and arrow_type.id in
-        [_Type_DATE32, _Type_DATE64, _Type_TIMESTAMP, _Type_DURATION])
+    coerce_to_ns = bool(options and options.get('coerce_temporal_nanoseconds', False))
 
     if getattr(arrow_type, 'tz', None):
         dtype = _get_pandas_tz_type(arrow_type, coerce_to_ns)