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This article was an effort to predict effluent quality parameters and analyze variables affecting mixed liquor volatile suspended solids (MLVSS) for Ekbatan wastewater treatment plant in Tehran, Iran. These parameters were predicted and analyzed using two of the most common classes of artificial neural networks (MLP and RBF) coupled with genetic algorithm. Temperature, pH, influent concentration of the parameters, sludge volume index (SVI), and sludge volume after 30  min of settling (V30) were inputs of the neural networks. These inputs were used to predict biochemical oxygen demand (COD), total nitrogen (TN), and total suspended solids (TSS) concentrations as well as MLVSS concentration in the aeration tank. The introduced models for training and testing data sets indicated an almost perfect match between the experimental and the predicted values of COD, TN, TSS, and MLVSS. The models were …