Rapid and reliable assessment of cyclone-induced damage remains a major challenge in coastal regions where cloud cover and accessibility limit conventional approaches. This study presents a coherence-based Synthetic Aperture Radar (SAR) framework for identifying as well as quantifying cyclone-induced surface changes in coastal districts in Bangladesh. Multi-temporal Sentinel-1 data were analyzed using interferometric coherence to capture spatial and temporal variations associated with five major cyclones: Amphan (2020), Sitrang (2022), Mocha (2023), Hamoon (2023), and Remal (2024). A time-series approach incorporating multiple pre-, during-, and post-event image pairs was implemented to distinguish cyclone-driven changes from natural surface variability and to improve detection reliability. The results reveal a consistent decline in coherence during cyclone events followed by gradual recovery. Spatial analysis indicates that areas of significant coherence loss correspond to zones affected by flooding, vegetation damage, and structural disruption. Estimated damage extents varied across cyclones, with Remal (2024) showing the largest affected area (~601 km²), followed by Mocha (~474 km²), Amphan (~288 km²), Sitrang (~122 km²), and Hamoon (~117 km²). Validation using field observations and secondary data yielded an overall classification accuracy of 94 %, confirming the robustness of the coherence-based approach. Complementary inundation mapping further demonstrated that coherence captures a broader spectrum of cyclone impacts beyond flood extent alone, including wind-induced damage and geomorphic changes. This study establishes multi-temporal SAR coherence analysis as a scalable, cloud-independent, and operationally effective tool for rapid post-cyclone damage assessment. The proposed framework enhances the reliability of disaster monitoring in complex coastal environments and offers strong potential for integration into near-real-time disaster response systems. Future research should focus on integrating multi-sensor datasets and long-term coherence time series to further refine damage characterization and reduce uncertainty.