Conducted under the SMEP-funded SAFECONOMY project, this study aimed to analyse the application of Gaussian Process Regression (GPR) modelling to improve the accuracy of degradation response predictions in wastewater treatment. Three crucial factors, i.e., catalyst (CFA–ZnF), oxidant (H₂O₂), and pollutant (MB) concentration, were selected to evaluate their impact on the response variable (degradation) using the GPR model. The superiority of the GPR model was validated by comparing the Gaussian Process Regression Mean (RPAE value) of 0.92689 with the Polynomial Regression Mean (RPAE value of 2.2947).

Besides, the simultaneous interpretation of the effects of the three predictors on the response variable was enabled using the GPR model, which is impossible when interpreting the polynomial regression model. Therefore, the GPR offers superior modeling, deeper insights, and reliable predictions, proving it to be a more sustainable and effective method for pollutant degradation in wastewater treatment than polynomial modeling.