This study presents a comprehensive analysis of the integrated Soil Moisture–Vegetation Health Index (SM-VHI) as a robust tool for drought detection and agricultural monitoring across East Africa using data from 2000 to 2020. A sensitivity analysis within the SM-VHI algorithm identified an optimal parameter weighting (α = 0.5), which improved detection accuracy, achieving a Critical Success Index (CSI) of 0.78. The SM-VHI exhibited strong correlations with independent drought indicators, including the Standardized Soil Moisture Index (SSMI), Vegetation Health Index (VHI), and one-month Standardized Precipitation-Evapotranspiration Index (SPEI-1), confirming its reliability in capturing agricultural drought dynamics and vegetation stress responses across diverse climatic conditions. Through spatial and temporal trend analyses, we identified patterns of drought severity and recovery, which emphasized the importance of tailored management strategies. Furthermore, the analysis incorporated historical maize yield data to evaluate the effectiveness of SM-VHI in representing agricultural drought conditions. A notable positive correlation (R = 0.45–0.72) was identified between SM-VHI anomalies and detrended maize yield throughout East Africa, suggesting that enhanced vegetation and soil moisture conditions are strongly linked to increased crop productivity. This validation demonstrates the capability of SM-VHI to effectively capture drought-induced yield variability. The findings confirm the effectiveness of SM-VHI as a reliable remote-sensing tool for monitoring drought conditions and have strong potential to inform agricultural practices and policy decisions aimed at enhancing food security in a changing climate.