Rising sea levels, extreme weather events and changes in wave patterns as direct consequences of climate change are putting increasing pressure on coastal areas. Consequently, the need for accurate risk assessment has become more urgent than ever. In this context, this study proposes a methodology for coastal risk assessment integrating satellite data and statistical modeling. The approach involves the collection and processing of data from active satellite altimetry missions, including CryoSat-2, Sentinel-3 and Sentinel-6 and the pioneering SWOT mission. In addition, ground displacement data from Sentinel-1 observations are used to monitor land subsidence along the coastal front, while Digital Elevation Models (DEMs) are incorporated to record terrain morphology. These datasets are integrated into a common geometric and height reference system and combined to estimate coastal, flood-prone, areas. The altimetry dataset is subjected to Kalman Filtering to identify linear trends and Bayesian Inference to detect non-linear trends. Finally, coastal erosion is studied through a simple “Bathtub” model, which incorporates the sea level and trend parameters, after they have been filtered and analyzed with the aforementioned methods, local subsidence and morphological variations in order to predict the future evolution of the coastline for the years 2050, 2100 and 2150. This integrated methodology provides an innovative and accurate approach to the assessment and management of coastal risks, offering valuable information for the protection of coastal areas from the effects of climate change.