The northern Bay of Bengal (BoB) is one of the world’s most climate-vulnerable regions. Communities along the coast of Bangladesh are frequently impacted by rising sea levels, stronger cyclones, salinity intrusion, and land subsidence, which threaten their homes, farms, and ecosystems. This research developed a flexible framework using machine learning and satellite data to better understand and manage these risks. By analyzing data from 2000 to 2025, including land use, elevation, rainfall, and socio-economic factors, the study mapped areas most at risk and created a Coastal Vulnerability Index (CVI). It also tested different adaptation strategies, like restoring mangroves and improving embankments, using computer-based models and geographic tools (such as GIS and Google Earth Engine). The results show that parts of southwestern Bangladesh, especially around Khulna and Satkhira, are the most vulnerable due to low elevation, fast shoreline erosion, and limited resources to adapt. This machine learning-based approach not only helps identify priority areas for action but also offers a model that can be used in other coastal regions facing similar threats. Ultimately, it aims to build long-term coastal resilience and reduce the impact of future climate hazards.