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The main objective of this work was to test different artificial neural network (ANN) based models, ie the ANN feed forward back propagation (ANN-FFBP), deep feed forward backpropagation (DFFBP), and deep cascade forward back propagation (DCFBP) models, for predicting the effluent quality of an upflow anaerobic sludge blanket-facultative pond (UASB-FP) system. The overall removal efficiency in the UASB-FP was> 84% at organic loading rates of∼ 26 kg d− 1. The chemical oxygen demand (COD), ammonical nitrogen (AN), total suspended solids (TSS), biochemical oxygen demand (BOD), total Kjeldahl nitrogen (TKN), and total phosphorus (TP) were inputs to each model, while the water quality characteristics of the UASB-FP effluent was used as the output. The dataset of 180 samples, collected over a one-year period, was utilized to train, test, and validate the developed models. Compared to ANN-FFBP …