This study presents a mathematical modeling framework to analyze extreme sea-level fluctuations in the Sea of Azov, driven by wind forcing, and enhances predictions through the integration of remote sensing data. The research addresses the critical need to improve the accuracy of surge and seiche predictions, phenomena that threaten coastal ecosystems and infrastructure in shallow basins. By solving shallow-water hydrodynamic equations and integrating satellite-derived observations with wind field data, we demonstrate that the combined use of numerical modeling and remote sensing significantly enhances the precision of extreme sea-level forecasts. Validation against in situ measurements confirms the model’s ability to accurately replicate observed water level variations. The results highlight the importance of localized geographical features and wind dynamics in governing hydrodynamic processes, providing a foundation for more reliable forecasting under changing climatic conditions. Ultimately, this work advances the development of science-based mitigation strategies to reduce the ecological and socioeconomic risks posed by wind-induced surge and seiche events in vulnerable coastal regions.