Temperature control is significant to building energy conservation, and the accurate prediction of indoor temperature is the prerequisite for precise control of building temperature. Proposes a global indoor temperature prediction model based on computational fluid dynamics (CFD) and LightGBM algorithms to realize global temperature simulation and global temperature change prediction over time. The simplified CFD model is based on the space building structure, sensor accuracy range, and actual temperature control range, which can meet the accuracy requirements and solve data redundancy, making it more practical. On this basis, the LightGBM algorithm and LSTM algorithm are used to simulate the global temperature spatial sequence change law. To be specific, the LightGBM algorithm is employed to predict the temperature-time sequence changes to realize the global prediction of indoor temperature. The experiment utilizes the annual building operation data and indoor and outdoor temperature monitoring data of a tobacco storage warehouse to construct an indoor global temperature prediction model. Experimental results of the practical measured temperature data show that the temperature distribution accuracy coefficient of 5 h global forecast is 0. 955 4, and the temperature range accuracy coefficient of 60 h global predict is 0. 994 0. Compared with the ANN, BP, and LSTM algorithms, the average accuracy coefficient of the proposed model is improved by 0. 022 4~0. 014 7.