Imidazolium-based ionic liquids (ILs) are widely studied, for their enormous potential in electrolytes and battery innovations. Polymeric ionic liquids (PILs), which combine the strengths of ILs and the thermal-mechanical stability of polymer matrix, are yet to be researched thoroughly. Molecular Dynamics (MD), being one of the most popular computational techniques inmaterials science, has been broadly utilized to analyze the properties of materials on an atomistic scale. Different force fields (FF), or potential functions, represent the interatomic interactions of the molecules. Recently, by taking advantage of machine learning (ML) methods, neural network (NN) based potential functions have been developed. In this study, we use MD simulations to investigate the bulk and ion-transport properties of two types of PILs: Poly(1-butyl-3-methylimidazolium) bis(trifluoromethylsulfonyl)imide and Poly(1-butyl-3-methylimidazolium) Chloride (PolyBMIM TFSI and PolyBMIM Cl). Meanwhile, we benchmark ANI-2x NN potential against two empirical FF: All-atomic Optimized Potential for Liquids Systems (OPLS-AA) and Polarizable FF with Drude Oscillator model to provide some insights on how well ML potential works on charged macromolecules systems.