This chapter examines how Geospatial technologies combined with AI have been used to deal with some pressing issues coastal areas face, such as erosion and accretion rates, which are intensified due to storm surges and sea-level change due to Climate change. A combination of Object-Based Image Analysis (OBIA), Random Forest (RF) classification, and the Digital Shoreline Analysis System (DSAS) was employed to extract and analyze shoreline dynamics. The research quantifies shoreline movement through three key metrics: Net Shoreline Movement (NSM), End Point Rate (EPR), and Weighted Linear Regression (WLR). Derived results show that 55.5 km of shoreline experienced accretion, 41.3 km exhibited erosion, and 26.1 km remained stable. The highest erosion rates were observed in Bheemunipatnam and Maharanipeta, while significant accretion was noted in Seethammadhara and Peddagantyada. The mean erosion is − 2.17 m/year, and the accretion is 3.2 m/year under WLR calculations. The RF classification helped in the auto-extraction of shorelines, while the image segmentation provided accurate shorelines and reduced the digitization errors, with a validation accuracy exceeding 95%. This highlights the significance of GIS and AI integration in shoreline change detection and monitoring.