diff --git a/docs/src/api.md b/docs/src/api.md
index fc2b4e7..60f46bc 100644
--- a/docs/src/api.md
+++ b/docs/src/api.md
@@ -6,6 +6,8 @@ SymbolicNeuralNetwork
 @SymbolicNeuralNetwork
 multi_layer_feed_forward
 ModelingToolkitNeuralNets.isneuralnetwork
+ModelingToolkitNeuralNets.hasneuralnetwork
 ModelingToolkitNeuralNets.isneuralnetworkps
+ModelingToolkitNeuralNets.hasneuralnetworkps
 ModelingToolkitNeuralNets.get_nn_chain
 ```
diff --git a/src/nn_par_accessors.jl b/src/nn_par_accessors.jl
index 9515bed..32d8080 100644
--- a/src/nn_par_accessors.jl
+++ b/src/nn_par_accessors.jl
@@ -24,6 +24,25 @@ function isneuralnetwork(p::Symbolics.SymbolicT)
     getmetadata(p, NeuralNetworkParameter, false)
 end
 
+"""
+    ModelingToolkitNeuralNets.hasneuralnetwork(p)
+
+Returns `true` if the parameter has the `neuralnetwork` metadata set (whenever the value is `true` or `false`). This function is primarily intended for internal use within dependent packages.
+
+Example:
+```julia
+@parameters d
+@SymbolicNeuralNetwork NN, θ = chain
+ModelingToolkitNeuralNets.hasneuralnetwork(d) # false
+ModelingToolkitNeuralNets.hasneuralnetwork(NN) # true
+ModelingToolkitNeuralNets.hasneuralnetwork(θ) # false
+````
+"""
+hasneuralnetwork(p::Union{Symbolics.Num, Symbolics.Arr, Symbolics.CallAndWrap}) = hasneuralnetwork(Symbolics.unwrap(p))
+function hasneuralnetwork(p::Symbolics.SymbolicT)
+    hasmetadata(p, NeuralNetworkParameter)
+end
+
 """
     ModelingToolkitNeuralNets.isneuralnetworkps(p)
 
@@ -43,6 +62,25 @@ function isneuralnetworkps(p::Symbolics.SymbolicT)
     getmetadata(p, NeuralNetworkParametrisation, false)
 end
 
+"""
+    ModelingToolkitNeuralNets.hasneuralnetworkps(p)
+
+Returns `true` if the parameter has the `neuralnetworkps` metadata set (whenever the value is `true` or `false`). This function is primarily intended for internal use within dependent packages.
+
+Example:
+```julia
+@parameters d
+@SymbolicNeuralNetwork NN, θ = chain
+ModelingToolkitNeuralNets.hasneuralnetworkps(d) # false
+ModelingToolkitNeuralNets.hasneuralnetworkps(NN) # false
+ModelingToolkitNeuralNets.hasneuralnetworkps(θ) # true
+````
+"""
+hasneuralnetworkps(p::Union{Symbolics.Num, Symbolics.Arr, Symbolics.CallAndWrap}) = hasneuralnetworkps(Symbolics.unwrap(p))
+function hasneuralnetworkps(p::Symbolics.SymbolicT)
+    hasmetadata(p, NeuralNetworkParametrisation)
+end
+
 
 ### Defines Other Accessors ###
 
diff --git a/test/nn_ps_accessors.jl b/test/nn_ps_accessors.jl
index 269feab..5e58f29 100644
--- a/test/nn_ps_accessors.jl
+++ b/test/nn_ps_accessors.jl
@@ -9,7 +9,9 @@ let
     @parameters p q[1:3]
     for s in [X, Y, Y[1], p, q, q[1]]
         @test !ModelingToolkitNeuralNets.isneuralnetwork(s)
+        @test !ModelingToolkitNeuralNets.hasneuralnetwork(s)
         @test !ModelingToolkitNeuralNets.isneuralnetworkps(s)
+        @test !ModelingToolkitNeuralNets.hasneuralnetworkps(s)
     end
 
     # Tests on MTKNeuralNets parameters
@@ -23,9 +25,18 @@ let
     @test !ModelingToolkitNeuralNets.isneuralnetwork(θ)
     @test ModelingToolkitNeuralNets.isneuralnetwork(U)
     @test !ModelingToolkitNeuralNets.isneuralnetwork(p)
+    @test ModelingToolkitNeuralNets.hasneuralnetwork(NN)
+    @test !ModelingToolkitNeuralNets.hasneuralnetwork(θ)
+    @test ModelingToolkitNeuralNets.hasneuralnetwork(U)
+    @test !ModelingToolkitNeuralNets.hasneuralnetwork(p)
     @test !ModelingToolkitNeuralNets.isneuralnetworkps(NN)
     @test ModelingToolkitNeuralNets.isneuralnetworkps(θ)
     @test !ModelingToolkitNeuralNets.isneuralnetworkps(U)
+    @test ModelingToolkitNeuralNets.hasneuralnetworkps(p)
+    @test !ModelingToolkitNeuralNets.hasneuralnetworkps(NN)
+    @test ModelingToolkitNeuralNets.hasneuralnetworkps(θ)
+    @test !ModelingToolkitNeuralNets.hasneuralnetworkps(U)
+    @test ModelingToolkitNeuralNets.hasneuralnetworkps(p)
 end
 
 # Check that `isneuralnetwork` and `isneuralnetworkps` give correct input on parameters stored in a model created using symbolic approach.
@@ -49,10 +60,18 @@ let
     @test !ModelingToolkitNeuralNets.isneuralnetwork(sys.d)
     @test ModelingToolkitNeuralNets.isneuralnetwork(sys.NN)
     @test !ModelingToolkitNeuralNets.isneuralnetwork(sys.θ)
+    @test !ModelingToolkitNeuralNets.hasneuralnetwork(sys.X)
+    @test !ModelingToolkitNeuralNets.hasneuralnetwork(sys.d)
+    @test ModelingToolkitNeuralNets.hasneuralnetwork(sys.NN)
+    @test !ModelingToolkitNeuralNets.hasneuralnetwork(sys.θ)
     @test !ModelingToolkitNeuralNets.isneuralnetworkps(sys.X)
     @test !ModelingToolkitNeuralNets.isneuralnetworkps(sys.d)
     @test !ModelingToolkitNeuralNets.isneuralnetworkps(sys.NN)
     @test ModelingToolkitNeuralNets.isneuralnetworkps(sys.θ)
+    @test !ModelingToolkitNeuralNets.hasneuralnetworkps(sys.X)
+    @test !ModelingToolkitNeuralNets.hasneuralnetworkps(sys.d)
+    @test !ModelingToolkitNeuralNets.hasneuralnetworkps(sys.NN)
+    @test ModelingToolkitNeuralNets.hasneuralnetworkps(sys.θ)
 end
 
 # Check that `isneuralnetwork` and `isneuralnetworkps` give correct input on parameters stored in a model created using NNBlock approach.
@@ -83,6 +102,15 @@ let
     @test !ModelingToolkitNeuralNets.isneuralnetwork(sys_nnblock.x)
     @test !ModelingToolkitNeuralNets.isneuralnetwork(sys_nnblock.y)
 
+    @test ModelingToolkitNeuralNets.hasneuralnetwork(sys_nnblock.nn.lux_apply)
+    @test !ModelingToolkitNeuralNets.hasneuralnetwork(sys_nnblock.nn.lux_model)
+    @test !ModelingToolkitNeuralNets.hasneuralnetwork(sys_nnblock.nn.p)
+    @test !ModelingToolkitNeuralNets.hasneuralnetwork(sys_nnblock.nn.T)
+    @test !ModelingToolkitNeuralNets.hasneuralnetwork(sys_nnblock.α)
+    @test !ModelingToolkitNeuralNets.hasneuralnetwork(sys_nnblock.δ)
+    @test !ModelingToolkitNeuralNets.hasneuralnetwork(sys_nnblock.x)
+    @test !ModelingToolkitNeuralNets.hasneuralnetwork(sys_nnblock.y)
+
     @test !ModelingToolkitNeuralNets.isneuralnetworkps(sys_nnblock.nn.lux_apply)
     @test !ModelingToolkitNeuralNets.isneuralnetworkps(sys_nnblock.nn.lux_model)
     @test ModelingToolkitNeuralNets.isneuralnetworkps(sys_nnblock.nn.p)
@@ -91,6 +119,32 @@ let
     @test !ModelingToolkitNeuralNets.isneuralnetworkps(sys_nnblock.δ)
     @test !ModelingToolkitNeuralNets.isneuralnetworkps(sys_nnblock.x)
     @test !ModelingToolkitNeuralNets.isneuralnetworkps(sys_nnblock.y)
+
+    @test !ModelingToolkitNeuralNets.hasneuralnetworkps(sys_nnblock.nn.lux_apply)
+    @test !ModelingToolkitNeuralNets.hasneuralnetworkps(sys_nnblock.nn.lux_model)
+    @test ModelingToolkitNeuralNets.hasneuralnetworkps(sys_nnblock.nn.p)
+    @test !ModelingToolkitNeuralNets.hasneuralnetworkps(sys_nnblock.nn.T)
+    @test !ModelingToolkitNeuralNets.hasneuralnetworkps(sys_nnblock.α)
+    @test !ModelingToolkitNeuralNets.hasneuralnetworkps(sys_nnblock.δ)
+    @test !ModelingToolkitNeuralNets.hasneuralnetworkps(sys_nnblock.x)
+    @test !ModelingToolkitNeuralNets.hasneuralnetworkps(sys_nnblock.y)
+end
+
+# Specific `hasneuralnetwork` and `hasneuralnetworkps` tests.
+let
+    @parameters p1 [neuralnetwork = true] p2 [neuralnetwork = false] p3 [neuralnetworkps = true] p4 [neuralnetworkps = false] p5 p6
+    @test ModelingToolkitNeuralNets.hasneuralnetwork(p1)
+    @test ModelingToolkitNeuralNets.hasneuralnetwork(p2)
+    @test !ModelingToolkitNeuralNets.hasneuralnetwork(p3)
+    @test !ModelingToolkitNeuralNets.hasneuralnetwork(p4)
+    @test !ModelingToolkitNeuralNets.hasneuralnetwork(p5)
+    @test !ModelingToolkitNeuralNets.hasneuralnetwork(p6)
+    @test !ModelingToolkitNeuralNets.hasneuralnetworkps(p1)
+    @test !ModelingToolkitNeuralNets.hasneuralnetworkps(p2)
+    @test ModelingToolkitNeuralNets.hasneuralnetworkps(p3)
+    @test ModelingToolkitNeuralNets.hasneuralnetworkps(p4)
+    @test !ModelingToolkitNeuralNets.hasneuralnetworkps(p5)
+    @test !ModelingToolkitNeuralNets.hasneuralnetworkps(p6)
 end
 
 # Checks the `get_nn_chain` accessor function.