To improve the prediction accuracy of atmospheric pollutant SO₂, a combined forecasting model with optimal weights for atmospheric pollutant SO₂ was constructed, which was based on multiple air quality prediction (WRF-CHEM, CMAQ, CAMx) modes according to the principle of minimum square sum of combined forecasting errors of each single air quality prediction model in the past years. The actual observation data and the aforementioned three air quality modes prediction data of Chuxiong, Zhaotong, and Mengzi stations in Yunnan Province from January to May in 2018 were selected as the experimental samples. Then the multivariate linear regression method and the dynamic weight updating method were used to compare the prediction results with the optimal weighted combination prediction method proposed in this study under the same experimental conditions. The experimental results show that the predicted values of the proposed method are closer to the observed values than those of the other two methods, and the two error evaluation indexes are the smallest. Generally speaking, the combined forecasting model with optimal weights synthesizes the advantages of each single air quality forecasting model and improves the forecasting accuracy of SO₂.