Biogas is a renewable energy source with significant production potential from various waste materials, including food waste. In this context, this study presents the development of three distinct models using Artificial Neural Networks (ANNs), capable of predicting the cumulative volume of biogas, methane, and CH4 concentration, respectively. A literature-based database was constructed, including variables from anaerobic digestion processes: biomass type, reactor/feed type, volatile solid content, pH, organic loading rate, hydraulic retention time, temperature, and reactor volume. For each set of models, 24 ANNs were developed and tested using the MATLAB computational tool. The ANNs estimation capability was assessed using the coefficient of determination (R2) and the sum of squared errors (SSE). Following initial stages, neural networks were employed to create response surfaces, aiming to identify optimal regions for biogas and methane production. However, a single model failed to achieve the desired representativeness, leading to data segmentation based on biomass type. The developed ANNs demonstrated effectiveness in estimating the groups used for training, testing, and validation. The best network achieved R2 values of 0.9969 for biogas, 0.9963 for methane, and 0.9386 for methane percentage, with SSE values of 0.1808, 0.1089, and 11.45, respectively. The strategy of combining process variables in response surfaces proved valuable in identifying optimal points in the production process.