Predictive capability of response surface methodology (RSM) and ant colony optimization combined with support vector regression (ACO-SVR) models are applied for determining optimal parameters in the process of heterogeneous Fenton oxidation of melanoidin, a high molecular weight polymer widely produced during fermentation processes generating large quantities of wastewater with intense brown color and extremely high chemical oxygen demand (COD). Prediction of the performance of nano zero-valent iron supported on activated carbon cloth-chitosan (ACC-CH-nZVI) catalysts was carried out using Box-Behnken design (BBD) and analysis of variance to evaluate the interaction of independent variables involved in heterogeneous Fenton reaction. The optimized condition with minimal consumption of H2O2 (173 mM) resulted in 77.1% decolorization of melanoidin-contaminated water corresponding to 74.4% COD removal at pH 3 (600 mg/l Fe dosage) for 90 min reaction time. The corresponding weight ratio of H2O2 to COD was 0.98, much lower than the stoichiometric value 2.125, indicating the effectiveness of ACC-CH-nZVI as a heterogeneous Fenton-like catalyst. In comparison to previously published experimental results, ACO-SVR model shows higher coefficient of determination (R-2; 0.9983) but lower root mean squared error (RMSE) and mean absolute error (MAE) than those of RSM model, indicating relative superiority in prediction capability. Besides, ACO algorithm appears to be a promising tool for improving forecasting accuracy of SVR model. This work demonstrates the applicability of ACO-SVR model in predicting the performance of wastewater treatment using Fenton process with limited number of experiment and exhibits satisfactory prediction results.