The coastal regions of the Mediterranean Sea rely heavily on groundwater, but excessive use for drinking water and agriculture is leading to depletion, salinization, drought, and subsidence. This disrupts ecosystem services and threatens water security for both people and nature. Climate change, shifting precipitation patterns, and sea-level rise are intensifying the crisis, impacting agriculture, the economy, ecology, and public health. A thorough understanding of coastal groundwater vulnerability is essential for sustainable development. Innovative modeling tools and data-driven insights would help stakeholders understand and address the impacts of human activities and climate change on groundwater. In this project, initiated by UNESCO under the GEF ID#9686: “Management of Coastal Aquifers and related Ecosystems in the Mediterranean”, we will develop models that can simulate complex hydrogeological processes, predicting the extent and impact of saltwater intrusion on freshwater resources under various scenarios, while also identify features like Fresh Offshore Groundwater and Submarine Groundwater Discharge (as important for ecosystems). Developments of parallel open-source SEAWAT (10.1016/j.advwatres.2021.103976) has indicated a breakthrough in variable-density groundwater flow and salt transport modelling. Now, huge models can be split into multiple (practically at least tens of) partitions and executed in parallel, leading to enormous reduction in computation time, and thus, making it possible to simulate groundwater salinity dynamics over a full glacial-interglacial cycle (viz. approx. 125 kyears). Given high performance computing facilities (supercomputers), this opens possibilities in building (high temporal and spatial scale) Large-scale Coastal Groundwater Models (LCGMs). An increasing number of open-source global hydrogeological datasets available on web portals will be used. Open-source tools (e.g., Python) will incorporate these datasets, like HydroBASINS (global-watershed-boundaries), CoPerm (geological parameters), and GEBCO (global DEM), as input into the models. In addition, AI Large Language Models data-mining techniques will make it possible to retrieve hydrogeological data (e.g., borelogs, salinity values) from articles as well as grey literature. Additionally, innovative groundwater salinity data collection techniques using Airborne EM surveys and citizen-science generated data using smartphone apps and web portals will be added too. T