<p>Recurrent flooding in Southeast Asia has a significant impact on rice production, particularly in flood-prone regions like West Bengal, India. This state, a major rice producer, experiences frequent floods, making its districts highly vulnerable. Geospatial resource modeling offers an effective method for assessing natural resources in such areas. This study focused on Malda district, a flood-affected, rice-based ecosystem, analyzing key resources such as rice production, rainfall, land use, land cover, and soil nutrients. The research used secondary time-series data and primary survey data to model these resources. Statistical models, including the Gompertz and Autoregressive Integrated Moving Average with Explanatory Variables (ARIMAX), were applied alongside Inverse Distance Weighting (IDW) for spatial analysis. The IDW model revealed an increase in both water bodies and cropped areas in Malda between 2017 and 2021. Results showed that the northern part of the district received more rainfall, while the south experienced more rainy days. Soil nutrients, including nitrogen, phosphorus, and potassium, were found to be low to medium in the district’s eastern and southern parts. The Gompertz model showed a steady increase in rice productivity over the past two decades, while the ARIMAX (0, 2, 1) model indicated that rainfall positively impacted rice yields. Notably, rice productivity in flood-prone areas surpassed the district average, due to farmers adopting adaptive strategies like new cropping patterns to mitigate flood risks. This study provides a valuable framework for stakeholders and policymakers to develop resource-based adaptation strategies for sustainable rice cultivation in flood-prone ecosystems.</p> Graphical Abstract <p></p>