Vegetation plays a vital role in maintaining ecological stability, carbon cycling, and food security. However, vegetation dynamics are strongly influenced by large-scale climate oscillations, particularly the El Niño–Southern Oscillation (ENSO). Understanding vegetation responses to different ENSO phases is essential for assessing ecosystem resilience and supporting agricultural planning in monsoon-dependent regions. In this study, MODIS-derived vegetation products Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Fraction of Absorbed Photosynthetically Active Radiation (FPAR) and Gross Primary Productivity (GPP) for the period 2010–2021 were analyzed. Principal Component Analysis (PCA) was applied to integrate these indices into a single Vegetation Activity Component (VAC). Lagged Spearman correlations (ρ) were then calculated to examine the relationship between VAC and the Multivariate ENSO Index (MEI) across four agro-climatic zones within the Middle Gangetic Plain: Tarai, Eastern, North-Eastern, and Vindhyan Plains. The first principal component (PC1) explained 80% of the total variance, with NDVI emerging as the most sensitive variable of vegetation greenness. Lagged correlation spatial analysis showed that La Niña phases were associated with strong positive vegetation responses (ρ = 0.25–0.40), suggesting improved rainfall and soil moisture availability, especially in the Tarai and Eastern Plains. Conversely, El Niño phases resulted in weak to negative correlations (ρ = −0.1 to −0.2), indicating drought stress and delayed vegetation recovery in the Vindhyan and North-Eastern regions. These findings identify distinct ENSO-sensitive hotspots characterized by phase-specific vegetation stress and recovery patterns. The integration of PCA and remote sensing-derived vegetation indices offers a robust and scalable framework for monitoring ENSO-induced vegetation variability, supporting adaptive land and water resource management in monsoon-dependent ecosystems.