Electricity generation from solar chimneys is one of the renewable energy production methods that have become widespread in recent years. The correct determination of the location before the installation of solar chimneys is important for the efficiency of the energy to be produced. This study has attempted to produce a solution to this issue by using artificial intelligence that can be explained with tree-based regression methods. For this purpose, the chimney inlet temperature and chimney flow rate produced by the chimney according to the meteorological data taken from around a solar chimney established in Isparta were used. With the inlet temperature and flow rate of the solar chimney, the estimated power to be produced in the solar chimney can be calculated, and it can be determined whether the solar chimney installation area will be efficient or not. Tree-based adaptive boosting (Adaboost), gradient boosting, random forest, extreme gradient boosting (XGB), and bagging methods were used for solar chimney, chimney inlet temperature, and chimney flow rate estimation. The performances of the models were determined using the model evaluation methods mean absolute error, mean absolute percentage error (MAPE) and root mean square error. In the study, XGB with 0.0076 MAPE value for chimney inlet temperature and 0.047 MAPE value for chimney flow rate was proposed, and SHapley Additive exPlanations method, one of the explainable artificial intelligence methods, was applied on the proposed model.