To construct a comprehensive dataset for modeling PFAS adsorption, we integrated experimental data mining from literature with synthetic data generation derived from isotherm equations. This approach facilitated the development of a robust dataset encompassing distribution coefficients (Kd) and key physicochemical variables essential for understanding adsorption processes. The primary source of data was peer-reviewed literature reporting adsorption experiments for PFAS. These studies included experimental details such as adsorption isotherms, BET surface area, and equilibrium concentration ranges (Ce). Adsorption isotherms, including widely used models such as the Freundlich equation (Qe = Kf * Ce 1/n ), provided the foundation for deriving additional data points. From the literature, isotherm parameters (Kf, 1/n) were extracted through regression fitting of experimental data. Using the established isotherm equations, equilibrium concentrations (Ce) spanning the reported range in each study were substituted to calculate corresponding Qe values, representing the amount of PFAS adsorbed per unit mass of absorbent. The calculated Qe values were then used to determine the distribution coefficient (Kd), a critical parameter reflecting the equilibrium partitioning of PFAS between the solid and liquid phases (Kd = Qe/Ce). This process was repeated systematically to generate synthetic data, ensuring that each isotherm contributed at least 15 data points within its experimental range. This interpolation strategy enriched the dataset while maintaining consistency with the original experimental framework. Table lists studies from which the data was mined for Kd. 2580 data points were collected from 172 isotherms obtained from more than 30 journal articles published on 73 unique adsorbents and 10 PFAS compounds (PFOS, PFOA, PFHxS, PFHxA, PFBS, PFBA, Gen-X, PFHpA, PFDA, and PFDoDA) between .