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In this study, data-driven deep learning methods were applied in order to model and predict the treatment of real municipal wastewater using anaerobic membrane bioreactors (AnMBRs). Based on the one-year operating data of two AnMBRs, six parameters related to the experimental conditions (temperature of reactor, temperature of environment, temperature of influent, influent pH, influent COD, and flux) and eight parameters for wastewater treatment evaluation (effluent pH, effluent COD, COD removal efficiency, biogas composition (CH₄, N₂, and CO₂), biogas production rate, and oxidation-reduction potential) were selected to establish the data sets. Three deep learning network structures were proposed to analyze and reproduce the relationship between the input parameters and output evaluation parameters. The statistical analysis showed that deep learning closely agrees with the AnMBR experimental …