To address the challenges of large-scale drought-resistance phenotyping in cotton—specifically the difficulty of assessment and the high cost of destructive sampling—this study developed a high-throughput evaluation framework for the Drought Resistance Coefficient of Effective Boll Number (DCEBN) in Gossypium hirsutum (upland cotton). This framework integrates time-series UAV-based multispectral technology with interpretable machine learning algorithms. Based on field drought experiments conducted in Xinjiang during 2023–2024 using 150 core germplasm accessions, we extracted and calculated the relative response ratios of canopy multispectral vegetation indices (DCVIs). We compared the predictive performance of K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Random Forest (RF), and Stacking ensemble algorithms, while employing the SHapley Additive exPlanations (SHAP) framework to quantify the contribution of each vegetation index to DCEBN prediction. This framework provides a practical, non-destructive approach for large-scale DCEBN prediction and for identifying key spectral indicators of drought tolerance in cotton. The results demonstrated that time-series multispectral features sensitively captured the dynamic response of cotton to water deficit; notably, ensemble learning models (RF and Stacking) exhibited superior fitting accuracy and generalization capabilities in predicting DCEBN. SHAP analysis identified NRI, SCCCI, MTVI2, and EVI as the four key indices contributing most significantly to the predictions, offering interpretable guidance for reducing spectral redundancy in future phenotyping. Validation using extreme-phenotype genotypes further confirmed that changes in these four indices effectively reflected the decline in photosynthetic rate and the reduction in effective boll number under drought stress, demonstrating that the model can both predict DCEBN and identify extreme germplasm for breeding use.