1) Hybrid RSM-ML models significantly improved PFAS adsorption prediction accuracy, with the Meta HOP-RSM-GB model achieving near-perfect performance (R² = 1.00, RMSE = 10.59, MSE = 112.21, MAE = 3.95), outperforming both standalone RSM and machine learning models. This demonstrated the power of combining statistical and ML-driven techniques in adsorption modeling. 2) Surface area and hydrophobicity (log Kow) were identified as dominant factors influencing PFAS adsorption, with higher log Kow values correlating with greater Kd regardless of surface area. This highlighted the importance of PFAS physicochemical properties in determining adsorption efficiency. 3) Electrostatic interactions, governed by pH and pHpzc, played a critical role in adsorption efficiency. Adsorption was enhanced at low pH and high pHpzc due to stronger electrostatic attraction, while high pH led to significant desorption, reinforcing the need for pH control in remediation strategies. 4) Response surface analysis revealed significant non-linear interactions between adsorption parameters, indicating that one-variable-at-a-time (OFAT) approaches are insufficient for optimizing PFAS removal. Instead, data-driven modeling is essential for capturing complex adsorption behaviors in a multiparameter process. 5) Hybrid models, particularly RMSE-based and meta-model approaches, provided the most accurate predictions of Kd by effectively integrating polynomial regression with ensemble learning. This highlighted the value of machine learning in addressing the limitations of traditional statistical modeling in adsorption studies. 6) Higher-order polynomial (HOP-RSM) regression alone exhibited poor predictive performance (R² = 0.04), underscoring the necessity of hybrid approaches to accurately model PFAS adsorption across a wide range of conditions. 7) Surface plots confirmed that adsorption optimization required balancing adsorbent surface chemistry and operational conditions, with the highest Kd values observed at moderate surface areas (1000 -1500 m²/g), pHpzc values (6 -10), and temperatures (30 -35 °C). 8) Machine learning hybridization with RSM can significantly reduce the number of experiments required for process optimization, offering a scalable approach for accelerating PFAS remediation research and minimizing experimental costs. 9) This study established hybrid RSM-ML modeling as a powerful framework for optimizing PFAS adsorption, providing a pathway for improved adsorbent design, predictive adsorption modeling, and data-driven decision-making in environmental remediation efforts. 10) Surface Area and Initial Concentration consistently emerge as the most influential features in determining the PFAS distribution coefficient (Kd), while pHpzc, pH, and molecular descriptors (pKa, Log Kow, Molecular Weight) provide additional insights into adsorption behavior. Across all three algorithms -Random Forest, Gradient Boost, and Extreme Gradient Boost-the data underscore that both adsorbent surface chemistry and PFAS properties significantly modulate Kd values.