Nonlinear autoregressive exogenous (NARX) neural network models were used to forecast the time-series profiles of anaerobic digestion-elutriated phase treatment (ADEPT). Experimental data from the operation of the pilot plant and lab-scale reactor were used for calibration, validation, and practice tests. Anaerobic digestion-elutriated phase treatment removed approximately 87% of volatile solids with a relatively brief hydraulic retention time of 7 days. The self-built machine learning algorithm provided confident predictions of the volatile-solids removal efficiency, biogas production, and methane content, with mean square error values of 0.32, 0.02, and 0.16, respectively. Time-series simulations of nonlinear autoregressive exogenous models demonstrated that ADEPT can improve organic removal and biogas production by maintaining the pH at 6.0-6.5 and 7.0-7.5 in the acidogenesis and methanogenic reactors, respectively. Applying nonlinear autoregressive exogenous neural network models to ADEPT allows high-rate anaerobic digestion without over-acidification.