Bandgap determines the suitability of materials for device applications such as \cite{lyu2021predictive} light-emitting diodes (LED), solar cells, and photo-detectors. The accuracy of the bandgap predicted using standard LDA or GGA functional is underestimated by density functional theory (DFT) when compared to experimental values. However, DFT combined with Machine Learning (ML) allows computational screening of materials with better accuracy. The training data for the models is obtained from density functional theory calculations which consist of A, B, and X-site elemental properties. The feature importance procedure screens the relative important features among all input features considered in the study. CatBoost(CB) regression model, \cite{Catboost} is an open-source library for gradient boosting. It gives high-perfromance on decision tress as it is based on gradient boosting algorithm and is suitable for small data sets as reduces overfitting, is implemented that can accurately predict the bandgap of $A_2BX_6$ perovskite. Eleven ML techniques were implemented, and their predictions of energy bandgap were compared, such as gradient boosted, random forest, support vector, AdaBoost, linear, K-nearest neighbor, kernel ridge, and decision tree. As per the study, the best performance is achieved by CatBoost regression model.