This article presents a novel approach for parameter extraction and optimization of perovskite solar cells (PSCs) using a hybrid random forest (RF) machine learning model integrated with a neural network algorithm. We validated our model’s accuracy through experiments on a fabricated cesium lead chloride perovskite cell, characterizing it under one Sun conditions and diffused light injection. We compared the root-mean-square error (RMSE) of our model with experimental measurements and existing literature on perovskites. Additionally, we utilized measured C–V characteristics as inputs for a polynomial regression algorithm to extract C–V coefficients. Our results indicate a significant improvement in RMSE to 0.00016 with hyperparameter optimization (HPO), representing nearly a 90% enhancement over the equilibrium optimizer (EO) benchmark. Although the computational time for the ML model with HPO was approximately double that of the EO, the ML model without HPO still outperformed the EO by about 25% with only a modest increase in time. These findings underscore the effectiveness of hybrid machine learning approaches in optimizing PSC performance.