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The integration of machine learning (ML) models in studying, investigating, and optimizing various electronic devices and materials has significantly glow up. With the aid of ML algorithms and input datasets, data regression and prediction can show the output characteristic performance under a wide range of input combinations. Herein, we utilize a random‐forest ML algorithm to study the influence of nine input design parameters on the overall power conversion efficiency (PCE) of cesium lead halides perovskites cells. The doping levels, the defects densities, and the thicknesses among the perovskite thin film, as well as the hole and electron transport layers, are studied against the cell PCE. The seeded dataset is managed using experimental data and experimentally validated numerical simulations. Datasets of more than 1 512 000 points were generated and seeded to the ML model. The PCE variation against the inputs for the three metal halides was investigated. A 17.8% PCE for CsPbI3 was reached, while PCE of 14.6% and 6.5% were recorded for CsPbBr3, and CsPbCl3, respectively.