This study aims to develop a comprehensive RSM-ML framework for modeling and optimizing the adsorption of PFAS on various adsorbent materials. The specific objectives are to: Investigate the effects of adsorbent properties, PFAS characteristics, and process conditions on the distribution coefficient (Kd) using RSM; Implement and compare hybrid RSM-ML algorithms, including RF, GB, and XGB, to predict Kd and evaluate their performance relative to RSM; Develop hybrid models to enhance predictive accuracy and capture complex variable interactions; and Optimize process parameters to maximize PFAS distribution coefficient and provide insights into variable significance for guiding future research and development.