Based on remote sensing, reanalysis and soil moisture site data, an integrated drought monitoring model on monthly scale was constructed for vegetation of growing seasons in different ecological zones of Hebei Province from 2001 to 2022. Four classic machine learning methods, namely, support vector machine (SVM), random forest (RF), radial basis function neural network (RBFNN) and extreme learning machine (ELM) were adopted for the modeling. Then combined with Sen’s slope analysis, the spatial variation of drought in Hebei Province was revealed. The results showed that the fitting effect of RF, RBFNN and SVM presented strong stability in different months of the growing season, as well as in different ecological zones, while that of ELM was relatively poor. Therefore, the integrated RF-RBFNN-SVM model was selected based on different ecological zones for the modeling of comprehensive drought monitoring in Hebei Province. The application of the model indicated that its fitting performance was better in the wet environment, and in the middle of the vegetation growing season. The average accuracy of the model was 85.15%, and the predicted value was in good agreement with the measured value. The average spatial variation of SM10 was 0.174/a in the study period, and soil relative humidity in 77.21% of the area showed an upward trend, and the drought situation was alleviated.