Per- and polyfluoroalkyl substances (PFAS), a class of synthetic fluorinated compounds termed “forever chemicals”, have garnered significant attention due to their persistence, widespread environmental presence, bioaccumulative properties, and associated risks for human health. Their presence in aquatic ecosystems highlights the link between human activity and the hydrological cycle. They also disrupt aquatic life, interfere with gas exchange, and disturb the carbon cycle, contributing to greenhouse gas emissions and exacerbating climate change. Federal agencies, state governments and non-government research and public interest organizations have emphasized the need for documenting the sites and the extent of PFAS contamination. However, the time-consuming nature of data collection, curation and analysis poses challenges for rapid identification of locations most impacted by PFAS contamination and most in need of appropriate remediation. To address this data limitation, our study leverages a novel geospatial dataset, machine learning models including frameworks such as CatBoost, IBM-NASA's Prithvi and UNet, and geospatial analysis to predict regions with high PFAS concentrations in surface water. Using data from the National Rivers and Streams Assessment (NRSA) and Michigan PFAS Action Response Team (MPART) Surface Water datasets, our analysis suggests the potential value of machine learning based models for targeted deployment of sampling investigations and remediation efforts.