To complement the RSM framework and address its limitations in capturing nonlinear and highorder interactions, machine learning (ML) algorithms were employed. The ML models used in this study included random forest (RF), gradient boosting (GB), and extreme gradient boosting (XGB), which have demonstrated strong performance in modeling complex environmental processes. These models were trained to predict Kd based on the same independent variables used in the RSM analysis. RF is an ensemble learning method that constructs multiple decision trees during training and averages their predictions to enhance accuracy and reduce overfitting. The algorithm's ability to handle multicollinearity and nonlinear relationships makes it particularly suitable for adsorption studies. GB builds sequential decision trees, with each tree correcting the errors of its predecessor. This iterative approach enables the model to achieve high predictive accuracy and uncover intricate relationships within the dataset. XGB, an optimized implementation of gradient boosting, incorporates advanced features such as regularization to prevent overfitting and parallel processing to improve computational efficiency. These characteristics make XGB a powerful tool for high-dimensional adsorption datasets.