Machine learning (ML) offers advanced capabilities for modeling complex adsorption processes by uncovering nonlinear relationships, variable interactions, and hidden patterns without relying on predefined equations. Popular algorithms such as random forest (RF), gradient boosting (GB), and extreme gradient boosting (XGB) show high predictive accuracy and can rank the influence of parameters (e.g., pH, hydrophobicity) on adsorption outcomes . However, large and diverse datasets are essential for accurate training an ML model, which can be resource-intensive in PFAS research. Integrating response surface methodology (RSM) with ML in hybrid models addresses this challenge: RSM generates structured datasets and identifies key variables, while ML enhances predictive performance by capturing nonlinearities and higherorder interactions. Although hybrid models have been successfully applied to areas like wastewater treatment and heavy metal adsorption, their use in PFAS adsorption remains limited, highlighting a promising avenue for future research.