The degradation of azo colorants, such as pomegranate red dye (PGRD), was studied in bicarbonate-activated hydrogen peroxide (BAP). This research is concerned with the modeling and optimization of the BAP oxidization system by an artificial neural networks (ANN) and genetic algorithm (GA) method. We employed a factorial design (FD) to analyze the influence of key process variables on the degradation efficiency of PGRD (η), including the mass of sodium bicarbonate (NaCHO3), the volume of hydrogen peroxide (H2O2), and the reaction temperature. The ANN prediction model of the degradation efficiency of PGRD was successful and demonstrates an excellent correlation with experimental response values of η, evidenced by a determination coefficient (R2) of 0.9433. Additionally, performance indicators such as the mean absolute error (MAE) of 1.09, mean square error (MSE) of 1.85, and root mean square error (RMSE) of 11.36 confirm that the ANN model is suitable for representing the degradation efficiency of PGRD in the BAP oxidation system. The optimal operating conditions were \(m_{{NaHCO_{3} }} = 90\;{\text{mg}}\) , \(V_{{H_{2} O_{2} }} = 0.4\;{\text{mL}}\) , and T = 50 ºC within 40 min of treatment, leading to a maxima degradation efficiency of PGRD (η) of 48.67%, with a total operating cost of 0.11 USD per liter. Furthermore, the abatement of PGRD in the BAP system follows a Behnajady-Modirshahla-Ghanbery (BMG) kinetic model, and the kinetic parameters values for A1 and A2 are 2.24 min and 1.941, respectively.