This work presents a neural network (NN) based extraction of the pion parton distribution function. The approach uses a flexible neural network parametrization to fit pion PDF data at the model scale, followed by NLO DGLAP evolution to obtain the pion PDF at experimental energy scales. The method provides a data-driven, model-independent extraction of pion structure.