When typhoons strike, heavy rainfall frequently triggers inundation disasters, causing significant impacts on agricultural crops. Efficiently understanding the effects of typhoon-induced flooding and implementing effective disaster mitigation measures are critical challenges for decision-makers. This study proposes a real-time dynamic inundation risk assessment framework to address these challenges, focusing on adaptation strategies and risk evaluation for paddy fields during typhoons. In the qualitative phase, governance levels and stakeholders are identified to facilitate discussions on key issues and protected targets. Both climatic and non-climatic factors contributing to hazards are considered. In the quantitative phase, hazard levels are defined, and the spatial distribution and vulnerability factors of paddy fields are assessed to quantify exposure and vulnerability. These three components are integrated using a risk assessment matrix to derive real-time dynamic risk levels. Yilan County, Taiwan, was selected as the study area to demonstrate the methodology. Forecasted values from a local AI-based inundation forecasting model served as the hazard component. The results reveal that the proposed approach delivers higher accuracy in predicting dynamic hazard and risk levels during severe inundation events. Furthermore, risk assessments incorporating hazard, exposure, and vulnerability provided more nuanced spatial distributions compared to those considering hazard alone. This highlights the framework's ability to integrate protected asset information, supporting decision-makers in devising timely and effective adaptation strategies, ultimately contributing to agricultural disaster management.