In parallel, the physicochemical properties of PFAS and adsorbent materials were also mined from the literature. BET surface area (m 2 /g) data were directly extracted, providing insights into the adsorbent's structural properties. Additional PFAS molecular properties, including Abraham solvation parameters (A, B, S, E, V), were computed using the ACD/Labs Percepta platform. These parameters quantify molecular interactions, such as hydrogen bonding (A, B), polarity/polarizability (S, E), and molecular size/volume (V), offering a detailed understanding of PFAS behavior during adsorption. This comprehensive methodology ensured that the dataset captured critical variables influencing adsorption processes. By combining experimental isotherm data with computational descriptors, this study bridges empirical insights and theoretical modeling. The resulting dataset supports advanced computational techniques, such as response surface methodology (RSM) and machine learning (ML), for predicting and optimizing PFAS adsorption across diverse adsorbent materials and environmental conditions.