The photoelectronic properties and corresponding applications of halide perovskites significantly depend on their band gaps and formation energy. However, experiments and density functional theory (DFT) calculations are usually time consuming and laborious to obtain these properties. In this study, the formation energy, band gap, and band gap classification label of halide double perovskites were predicted in terms of material parameters via using the gradient boosting tree combined with the genetic algorithm and grid search algorithm. The coefficients of determination (