This study investigates the combined effect of wheat straw particle size and mixing ratio on the anaerobic co-digestion (ACD) of waste activated sludge under mesophilic conditions. Ten batch digesters were tested with varying straw-to-sludge ratios (0–1.5%) and particle sizes (5 cm, 1 cm, and <2 mm). Fine straw particles (<2 mm) at 1.5% loading achieved the highest removal efficiencies for TS (43.55%), TVS (47.83%), and COD (51.52%), resulting in a 140% increase in biogas yield and methane content of 60.15%. The energy recovery reached 14.37 kWh/kg, almost double the control. The developed Recurrent Neural Network (RNN) model (3 layers, 13 neurons, 500 epochs) predicted biogas production with 99.8% accuracy, a root mean square error (RMSE) of 0.0038, a mean absolute error (MAE) of 0.0093, and an R2 close to 1. These results confirm the potential of integrating agricultural residues into wastewater treatment for renewable energy recovery and emission reduction. This study uniquely integrates mechanical pretreatment of wheat straw with RNN-based modeling to enhance biogas generation and predictive accuracy. It establishes a dual-experimental AI framework for optimizing sludge–straw co-digestion systems. This approach provides a scalable, data-driven solution for sustainable waste-to-energy applications.