The study investigates the impact of flooding on vegetation health and agricultural productivity in Hugli district, West Bengal, between 2022 and 2024 using remote sensing and statistical modeling techniques. Flooding is a recurrent hazard in the region, significantly affecting cropland, soil health, and ecosystem stability, necessitating a systematic assessment of flood-induced vegetation stress. The primary objective of this study is to analyze the relationship between flood intensity and vegetation damage using the Normalized Difference Flood Index (NDFI), Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), and Disaster Vegetation Damage Index (DVDI). The study utilizes Bhuvan data to derive spatio-temporal flood and vegetation indices, combined with GIS-based (Geographic Information System-based) buffer analysis and regression modeling to examine the extent of crop damage. The results indicate that flood severity increased in 2024, with the highest DVDI values (> 0.65) recorded in river-adjacent farmlands of Khanakul-II, Singur, and Chinsurah-Magra, while higher-elevation agricultural zones (e.g., Pandua, Tarakeswar) exhibited minimal damage (DVDI < 0.2). The regression model (R2 = 0.152, N is 1981) confirms a statistically significant negative correlation (− 0.63) between NDFI and DVDI, validating flood impact predictions. The findings emphasize the need for adaptive flood mitigation strategies, improved drainage infrastructure, and sustainable land-use planning. This study provides a quantitative basis for disaster management policies and agricultural resilience planning, while future research should incorporate hydrological modeling and long-term trend analysis for improved predictive accuracy.