Drought stress severely constrains cotton yield and fiber quality, but conventional evaluation methods are inefficient and time-consuming. To address this, we developed a high-throughput, non-destructive phenotyping framework by integrating UAV-based multispectral remote sensing with machine learning, using 225 upland cotton (Gossypium hirsutum L.) accessions. The accessions were subjected to well-watered (CK) and drought stress (DS) treatments at the flowering and boll-setting stage. Canopy multispectral imagery (Green/Red/Red_edge/Near-infrared bands) was acquired via DJI Mavic 3 Multispectral UAV, and 16 vegetation indices (VIs) were derived. Concurrently, 15 agronomic and fiber quality traits were measured to calculate drought resistance coefficients (DRCs), which were used for principal component analysis (PCA) and comprehensive drought tolerance index (D) construction. Hierarchical clustering categorized the accessions into 6 drought tolerance grades (Groups I–VI). Variable importance analysis identified GNDVI, NGRVI, and NDRE as the most drought-sensitive VIs (% IncMSE > 11). Among four regression models (LR, KNN, LGBM, XGBoost), XGBoost achieved the best performance for D prediction (test set: R² = 0.785, RMSE = 0.032, MAE = 0.024). This study demonstrates that UAV multispectral data coupled with XGBoost enables accurate, efficient drought tolerance assessment, providing a robust tool for high-throughput germplasm screening and smart agricultural management.