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Frequent spontaneous combustion activities restrict ecological restoration of coal waste dumps after reclamation. Effective monitoring of vegetation restoration is important for ensuring land reclamation success and preserving the ecological environment in mining areas. Development of unmanned aerial vehicle (UAV) technology has enabled fine-scale vegetation monitoring. In this study, we focused on Medicago sativa L. (alfalfa), a representative herbaceous vegetation type, in a coal waste dump after reclamation in Shanxi province, China. The alfalfa aboveground biomass (AGB) was used as an indicator for assessing vegetation restoration. The objective of this study was to evaluate the capacity of UAV-based fusion of RGB, multispectral, and thermal infrared information for estimating alfalfa AGB using various regression models, including random forest regression (RFR), gradient boosting decision tree (GBDT), K-nearest neighbor (KNN), support vector regression (SVR), and stacking models. The main results are as follows: (i) UAV multi-source data fusion improved alfalfa AGB estimation accuracy, although the enhancement diminished with the increasing number of sensor types. (ii) The stacking model consistently outperformed RFR, GBDT, KNN, and SVR regression models across all feature fusion combinations. It achieved high accuracy with R2 of 0.86–0.88, RMSE of 80.06–86.87 g/m2, and MAE of 60.24–62.69 g/m2. Notably, the stacking model based on only RGB imagery features mitigated the accuracy loss from limited types of features, potentially reducing equipment costs. This study demonstrated the potential of UAV in improving vegetation restoration management of coal waste dumps after reclamation. [ABSTRACT FROM AUTHOR]