An Insight Towards Transformative PFAS Remediation By Integrating Machine Learning Into Sustainable Practice

[1]¿p#1 The extreme environmental persistence and ubiquitous distribution of per- and polyfluoroalkyl substances (PFAS) in soil and water systems pose an urgent threat to ecosystem stability and human health. Conventional models such as sorption isotherms, convection–dispersion equations, and first-order degradation kinetics have improved our understanding of PFAS fate but often fail to capture nonlinear, site-specific behaviors, limiting their predictive power and research optimizations. This review synthesizes current knowledge on PFAS transport, chemistry and remediation, with a focus on emerging technologies (most effective and applicable) and data-driven approaches. We evaluate advanced oxidation, thermal desorption, and plasma-based destruction, highlighting their mechanisms, efficiencies, and limitations. With a central theme focused on the growing role of machine learning (ML) in overcoming the constraints of empirical modeling. ML techniques such as Random Forest, XGBoost, and deep learning are increasingly used to predict PFAS occurrence, identify key environmental drivers, and optimize treatment parameters under complex water matrix conditions. Despite promise, challenges remain, including data scarcity, model interpretability, and scalability. Here, we propose a hybrid framework that integrates mechanistic models with ML to enhance predictive accuracy, support site-specific decision-making, and guide the development of sustainable remediation strategies.