Data-driven strategies, particularly machine learning (ML), offer transformative opportunities to redesign the development, application, and regeneration of advanced resins for the selective removal of per- and polyfluoroalkyl substances (PFAS) from complex water matrices. Despite decades of progress, conventional resin-based treatment continues to face persistent challenges, including poor selectivity toward short-chain PFAS, trade-offs between adsorption capacity and regeneration efficiency, and prohibitive operational costs at scale. Here, we highlight how ML can decode complex “structure–property–performance” relationships to overcome these limitations, especially under realistic conditions where coexisting ions and natural organic matter (NOM) compromise resin performance. By integrating generative models, reinforcement learning, and hybrid digital twin frameworks, we can predict fouling risks and dynamically optimize operational parameters. This integration enables a paradigm shift toward adaptive resins through a synergistic, data-driven closed-loop design. By uniting theoretical, computational, and experimental approaches, such resins can respond to variable water chemistries while maintaining closed-loop reusability. This data-driven framework supports continuous optimization of PFAS capture, solvent recovery, and system longevity. Ultimately, we position ML not merely as a computational aid but also as a foundational enabler for scalable, cost-effective, and sustainable PFAS remediation, and we propose actionable insights to guide resin innovation in alignment with regulatory constraints and real-world operational demands.