Sargassum beaching is impacting tourism, fisheries, coastal ecosystems, coastal residents and businesses. Rapid effective and efficient removal of sargassum from beaches requires careful planning and coordination that includes predicting where sargassum beaching will occur and the quantities that will make landfall. This is a difficult but tractable problem which the team is undertaking by first applying existing models to the publicly available data from Antigua and Barbuda. Our Solution is to implement a simulator that will offer users the opportunity to interact with a dynamic interface, showcasing how different variables impact sargassum movement and beaching events in Antigua and Barbuda.  This simulator will be based on the machine learning models we develop, providing precise predictions for beaching events in Antigua and Barbuda, leveraging historical data. We envision that the proposed simulator system that combines data/model insights from multiple sources will help the decision-makers in Eastern Caribbean regions to track and predict future sargasum adenuationa , and quantify its impact on economic activities such as tourism. This system will turn cutting-edge research into useable science and exploit the various data source from regions to build robust predictive models in sargassum drift and beaching pattern, and ultimately inform the strategic and operational level planning.