A library of metal oxide-conjugated polymer composites was synthesized, encompassing of WO3-polyaniline (PANI), WO3-poly(N-methylaniline) (PMANI), WO3-poly(2-fluoroaniline) (PFANI), WO3-polythiophene (PTh), WO3-polyfuran (PFu) and WO3-poly(3,4-ethylenedioxythiophene) (PEDOT). These composites were probed as hole selective layers for perovskite solar cells (PSCs) fabrication. We adopted machine learning approaches to predict and compare PSCs performances with the developed WO3 and its composites. The experimental and theoretical results are coherent, when the electro-optical properties of PSC were computed. Notably, for the evaluation of PSCs performance, decision tree model is the ideal for WO3-PEDOT composite, while random forest model was found to be suitable for WO3-PMANI, WO3-PFANI, WO3-PFu. While in the case for WO3, WO3-PANI and WO3-PTh, K Nearest Neighbors model was appropriate. Machine learning models can be a pioneering prediction models for the PSCs performance and its validation.