diff --git a/python/benchmarks/bench_arrow.py b/python/benchmarks/bench_arrow.py
index b59a43d42072..4ed1c91d579c 100644
--- a/python/benchmarks/bench_arrow.py
+++ b/python/benchmarks/bench_arrow.py
@@ -170,3 +170,46 @@ def peakmem_nested_ints_with_nulls_to_rows(self, n_rows, method):
 
     def peakmem_array_of_structs_to_rows(self, n_rows, method):
         self.convert(self.array_of_structs)
+
+
+class ArrowStructMapColumnToRowsBenchmark:
+    """
+    Benchmark for converting Arrow struct and map columns to Python rows.
+
+    ``baseline`` measures plain ``column.to_pylist()``; ``bulk`` measures
+    ``ArrowTableToRowsConversion._to_pylist`` with the struct/map bulk paths.
+    """
+
+    params = [
+        [100000, 1000000],
+        ["baseline", "bulk"],
+    ]
+    param_names = ["n_rows", "method"]
+
+    def setup(self, n_rows, method):
+        from pyspark.sql.conversion import ArrowTableToRowsConversion
+
+        self.structs = pa.array(
+            [{"a": i, "b": f"s{i}"} if i % 10 != 0 else None for i in range(n_rows)],
+            type=pa.struct([("a", pa.int64()), ("b", pa.string())]),
+        )
+        self.maps = pa.array(
+            [
+                [(f"k{i % 3}", i), (f"q{i % 5}", i + 1)] if i % 10 != 0 else None
+                for i in range(n_rows)
+            ],
+            type=pa.map_(pa.string(), pa.int64()),
+        )
+        if method == "bulk":
+            self.convert = ArrowTableToRowsConversion._to_pylist
+        else:
+            self.convert = lambda column: column.to_pylist()
+
+    def time_structs_to_rows(self, n_rows, method):
+        self.convert(self.structs)
+
+    def time_maps_to_rows(self, n_rows, method):
+        self.convert(self.maps)
+
+    def peakmem_structs_to_rows(self, n_rows, method):
+        self.convert(self.structs)
diff --git a/python/pyspark/sql/conversion.py b/python/pyspark/sql/conversion.py
index fddb42c63d8c..e660ddc18e12 100644
--- a/python/pyspark/sql/conversion.py
+++ b/python/pyspark/sql/conversion.py
@@ -1011,8 +1011,11 @@ def _should_manual_bulk() -> bool:
     @staticmethod
     def _to_pylist(column: Union["pa.Array", "pa.ChunkedArray"]) -> List[Any]:
         """
-        Equivalent to ``column.to_pylist()``, but converts (nested) list columns in bulk
-        instead of one scalar at a time.
+        Equivalent to ``column.to_pylist()``, but converts (nested) list, struct and map
+        columns in bulk instead of one scalar at a time. Structs become dicts (with
+        a fallback to ``to_pylist`` for duplicate field names, which raise ``ValueError``
+        there) and maps become lists of ``(key, value)`` tuples, matching
+        ``StructScalar.as_py`` and ``MapScalar.as_py`` exactly.
 
         Internal helper for the worker and ``convert`` call sites; do not use
         externally.
@@ -1043,9 +1046,44 @@ def _to_pylist(column: Union["pa.Array", "pa.ChunkedArray"]) -> List[Any]:
                 result.extend(ArrowTableToRowsConversion._to_pylist(chunk))
             return result
 
-        if (pa.types.is_list(column.type) or pa.types.is_large_list(column.type)) and len(
-            column
-        ) > 0:
+        if len(column) == 0:
+            return []
+
+        if pa.types.is_map(column.type):
+            # Maps have the same offsets layout as lists; each row becomes a
+            # list of (key, value) tuples, matching MapScalar.as_py.
+            n = len(column)
+            offsets = column.offsets.to_numpy(zero_copy_only=True).tolist()
+            start = offsets[0]
+            length = offsets[-1] - start
+            keys = ArrowTableToRowsConversion._to_pylist(column.keys.slice(start, length))
+            items = ArrowTableToRowsConversion._to_pylist(column.items.slice(start, length))
+            if column.null_count == 0:
+                return [
+                    list(
+                        zip(
+                            keys[offsets[i] - start : offsets[i + 1] - start],
+                            items[offsets[i] - start : offsets[i + 1] - start],
+                        )
+                    )
+                    for i in range(n)
+                ]
+            valid = column.is_valid().to_numpy(zero_copy_only=False).tolist()
+            return [
+                (
+                    list(
+                        zip(
+                            keys[offsets[i] - start : offsets[i + 1] - start],
+                            items[offsets[i] - start : offsets[i + 1] - start],
+                        )
+                    )
+                    if valid[i]
+                    else None
+                )
+                for i in range(n)
+            ]
+
+        elif pa.types.is_list(column.type) or pa.types.is_large_list(column.type):
             n = len(column)
             # List offset buffers never carry a validity bitmap, so this conversion is
             # always zero-copy; zero_copy_only=True asserts that invariant and would
@@ -1063,6 +1101,26 @@ def _to_pylist(column: Union["pa.Array", "pa.ChunkedArray"]) -> List[Any]:
                 for i in range(n)
             ]
 
+        elif pa.types.is_struct(column.type):
+            n = len(column)
+            names = [column.type.field(i).name for i in range(column.type.num_fields)]
+            if len(set(names)) != len(names):
+                # StructScalar.as_py raises ValueError on duplicate field names;
+                # let the generic path surface the same error.
+                return column.to_pylist()
+            fields = [
+                ArrowTableToRowsConversion._to_pylist(column.field(i))
+                for i in range(column.type.num_fields)
+            ]
+            if column.null_count == 0:
+                if not names:
+                    return [{} for _ in range(n)]
+                return [dict(zip(names, row)) for row in zip(*fields)]
+            valid = column.is_valid().to_numpy(zero_copy_only=False).tolist()
+            if not names:
+                return [{} if m else None for m in valid]
+            return [dict(zip(names, row)) if m else None for row, m in zip(zip(*fields), valid)]
+
         return column.to_pylist()
 
     @staticmethod
diff --git a/python/pyspark/sql/tests/test_conversion.py b/python/pyspark/sql/tests/test_conversion.py
index 7ce2dfe01411..d02dd8e73497 100644
--- a/python/pyspark/sql/tests/test_conversion.py
+++ b/python/pyspark/sql/tests/test_conversion.py
@@ -15,6 +15,7 @@
 # limitations under the License.
 #
 import datetime
+import decimal
 import unittest
 import unittest.mock
 from zoneinfo import ZoneInfo
@@ -899,6 +900,37 @@ def test_matches_to_pylist(self):
             pa.array([], type=pa.list_(pa.int32())),
             pa.array([None, None], type=pa.list_(pa.string())),
             pa.array([[1, 2], None], type=pa.list_(pa.int64(), 2)),
+            # non-list leaves keep as_py semantics (native to_pylist)
+            pa.array([b"", None, b"\x00\xff"], type=pa.binary()),
+            pa.array([datetime.date(2020, 1, 2), None], type=pa.date32()),
+            pa.array([decimal.Decimal("1.23"), None], type=pa.decimal128(10, 2)),
+            pa.array([[b"x", None], None, [b""]], type=pa.list_(pa.binary())),
+            pa.array([[True, None], [False]], type=pa.list_(pa.bool_())),
+            # struct and map bulk paths
+            pa.array(
+                [{"a": 1, "b": "x"}, None, {"a": None, "b": None}],
+                type=pa.struct([("a", pa.int64()), ("b", pa.string())]),
+            ),
+            pa.array(
+                [{"s": {"a": 1}, "l": [1, None]}, None],
+                type=pa.struct(
+                    [("s", pa.struct([("a", pa.int32())])), ("l", pa.list_(pa.int64()))]
+                ),
+            ),
+            pa.array([{}, None, {}], type=pa.struct([])),
+            pa.array([None] * 4, type=pa.struct([("a", pa.int32())])),
+            pa.array(
+                [[("k1", [1, None]), ("k2", None)], None, []],
+                type=pa.map_(pa.string(), pa.list_(pa.int32())),
+            ),
+            pa.array(
+                [{"m": [("k", 1)]}, None],
+                type=pa.struct([("m", pa.map_(pa.string(), pa.int64()))]),
+            ),
+            pa.array(
+                [[{"a": 1}, None], None],
+                type=pa.list_(pa.struct([("a", pa.int64())])),
+            ),
         ]
         for column in columns:
             views = [column, column.slice(1), column.slice(0, max(len(column) - 1, 0))]
@@ -922,6 +954,20 @@ def test_int_list_with_nulls_stays_int(self):
         self.assertEqual(result, [[1, None, 3]])
         self.assertEqual([type(v) for v in result[0]], [int, type(None), int])
 
+    def test_struct_duplicate_field_names_still_raises(self):
+        import pyarrow as pa
+
+        dup = pa.StructArray.from_arrays([pa.array([1, 2]), pa.array(["a", "b"])], names=["x", "x"])
+        with self.assertRaises(ValueError):
+            ArrowTableToRowsConversion._to_pylist(dup)
+
+    def test_struct_rows_are_distinct_dicts(self):
+        import pyarrow as pa
+
+        result = ArrowTableToRowsConversion._to_pylist(pa.array([{}, {}], type=pa.struct([])))
+        self.assertEqual(result, [{}, {}])
+        self.assertIsNot(result[0], result[1])
+
     def test_convert_table_with_list_columns(self):
         import pyarrow as pa