This research article focuses on enhancing the performance of Microbial Electrolysis Cells (MEC) for bio-hydrogen production and energy recovery. The study, conducted at Prince Sattam Bin Abdulaziz University in Al-Kharj, Saudi Arabia, and Minia University in Egypt, aimed to optimize three key parameters - buffer concentration, dilution factor, and applied voltage - crucial for MEC efficiency. By combining Artificial Gorilla Troops Optimization (AGTO) with Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling, the researchers successfully determined the optimal values of these parameters to boost bio-hydrogen production. The results indicated a significant improvement in hydrogen yield and energy recovery. The integration of ANFIS and AGTO reduced RMSE values, enhancing prediction accuracy compared to conventional methodologies like Response Surface Methodology (RSM) and Artificial Neural Network integrated with Particle Swarm Optimization (ANN-PSO). Notably, the RMSE for hydrogen yield decreased by 91.7% using ANFIS, with a substantial increase in R-square values. Moreover, the study demonstrated a 34.74% increase in hydrogen yield and a 29.9% enhancement in energy recovery when compared to results from RSM and ANN-PSO. The innovative approach not only optimized MEC performance but also showcased the potential of utilizing artificial intelligence and optimization techniques in biofuel production. Overall, the research highlights the importance of integrating advanced methodologies to achieve sustainable and efficient bioenergy production.