This study focuses on development, characterization and validation of an artificial neural network (ANN) model for prediction of advanced oxidation of organics in water matrix. The different ANNs, based on multilayer perceptron (MLP) and radial basis function (RBF) methodologies, have been applied for modeling of the behavior of complex system; zero-valent iron activated persulfate oxidation (Fe-0/S2O82-) of reactive azo dye C.I. Reactive Red 45 (RR45) in aqueous solution. The input variables for ANN modeling are corresponding to Fe-0/S2O82- process parameters such as pH, dosage of zero-valent iron and concentration of persulfate, while the system output is the mineralization extent of aqueous RR45 solution after the treatment by Fe-0/S2O82- at set conditions. The performance of developed ANN models has been compared and evaluated with regard the applied methodology, training algorithm, activation function and network topology. The results show that MLP methodology needs sinusoidal activation function to reveal the maximal capability. It is demonstrated that although ANN model based on RBF methodology offers good predictive ability, its capability to extrapolate is limited. The full potential of ANN modeling is reached using MLP methodology and scaled conjugate gradient training algorithm in combination with sinusoidal activation function, 6 hidden layer neurons and 8 experimental data points. Based on external validation set, it is demonstrated that the developed model is accurate with the average of relative error 1.70%, and there is no absolute or proportional systematic error.