This study deals with the modeling and simulation of an efficient pilot plant photo-chemical wastewater treatment reactor. Treatment of an azo dye (i.e. direct red 23) was performed using a UV/H2O2 process in a jet mixing photo-reactor with 10-L volume. To model the reactor and simulate the treatment process, six important, influential physical and chemical factors such as nozzle angle (theta(N)), nozzle diameter (d(N)), flow-rate (Q), irradiation time (t), H2O2 initial concentration ([H2O2](0)), and pH, were taken into account. In this regard, artificial neural networks (ANNs) were employed as a powerful modeling methodology. Six different ANN architectures were constructed and most appropriate numbers for hidden neuron and learning iteration were determined based on minimization of the mean square error (MSE) function related to the testing data sets. Furthermore, simulation of the reactor efficiency, as well as sensitivity analysis, was performed via the cross-validation outputs. It was found that a three-layered feed-forward ANN composes ten hidden neurons, calibrated at 100th iteration using "trainlm" as learning algorithm and "tansig" and "purelin" as transfer functions in the hidden and output layers can model the process as the best case. The order of importance for variation of the key factors were indicated as [H2O2](0)> t > pH > Q > theta(N) > d(N).