To accurately invert the soil moisture in Anhui Province and enhance the adaptability and accuracy of machine learning models, support vector regression, extreme gradient boosting, CatBoost, random forest, AdaBoost, and Stacking model (the first five machine learning models were selected as the base models, linear regression was used as the meta-model) were used to invert the soil moisture in Anhui Province. The spatial distribution and time series of soil moisture inversion results obtained from the Stacking model were analyzed. The results indicate that, with the same input remote sensing data, the Stacking model has higher accuracy and robustness compared to individual models. The correlation coefficient between the inverted soil moisture and the measured data reaches 0.72, and the root mean square error (RMSE) is 0.05 m³/m³. The spatial heterogeneity of soil moisture in Anhui Province is relatively high. The northern region is relatively dry, with an average soil moisture of around 0.2 m³/m³. The eastern areas of Chaohu Lake and the Yangtze River region are relatively humid, with an average soil moisture of up to 0.4 m³/m³. Although the Dabie Mountain area and the southern part of Anhui Province have higher altitudes, their soil moisture is still higher than that of the Huaibei Plain, indicating that the differences in climate between the north and the south may affect the magnitude of soil moisture. Overall, the spatial pattern of soil moisture in Anhui province is an increasing trend from the northwest to the southeast.