This study presents a broad investigation of coastal vulnerability dynamics, spanning two decades, with a concentrated emphasis on the Chennai region. The primary goal is to scrutinize the Coastal Vulnerability Index (CVI) trends, dissecting the impacts of cyclones and envisaging scenarios of future climate change. Employing machine learning techniques, this research combines diverse variables to craft a meticulous vulnerability map delineating the coastal region's susceptibility. Initial assessments accurately map vulnerability levels within the study area over a 20-year period, initially excluding cyclonic influences. Subsequently, a detailed analysis reveals the specific impact of cyclones on the CVI, contrasting vulnerability levels with and without their consideration. The study further investigates into future climate scenarios, particularly Representative Concentration Pathways (RCP), predicting their potential effects on the vulnerability index. Shoreline recession rates are computed for each scenario, offering insights into the anticipated coastal changes. Crucially, the research integrates geological, physical, and socio-economic variables, employing machine learning methodologies like Support Vector Machines, Random Forest, and Decision Trees to construct a holistic Coastal Vulnerability Index. This index forms the foundation for a detailed vulnerability map, serving as a crucial outcome, providing a simple illustration of vulnerability levels across the coastal region. The study's findings promise to enrich our understanding of coastal vulnerability dynamics, offering valuable insights for effective management strategies amidst evolving climatic scenarios.