In this study, an adaptive neuro-fuzzy inference system (ANFIS) including five process variables, such as influent chemical oxygen demand, influent flow rate, influent total Kjeldahl nitrogen, effluent volatile fatty acids and effluent bicarbonate, was described to predict the effluent chemical oxygen demand load from a full-scale expanded granular sludge bed reactor (EGSBR) treating corn processing wastewater. The proposed ANFIS model was conducted by applying hybrid learning algorithm and the model performance was tested by the means of distinct test data set randomly selected from the experimental domain. The ANFIS-based predictions were also validated using various descriptive statistical indicators, such as root mean-square error, index of agreement, the factor of two, fractional variance, proportion of systematic error, etc. The lowest root mean square error (RMSE = 0.03655) and the highest determination coefficient (R-2 = 0.958) were achieved with the subtractive clustering of a first-order Sugeno type fuzzy inference system. ANFIS predicted results were compared with the outputs of multiple nonlinear regression analysis-based models derived in the scope of the present work. Statistical performance indices computed for the testing data set proved that the developed ANFIS-based model exhibited a very good precision in predicting the effluent chemical oxygen demand, load for the EGSBR system. Due to high capability of the ANFIS model in capturing the dynamic behavior and non-linear interactions, it was demonstrated that a complex system, such as anaerobic digestion, could be easily modeled.