The research article focuses on enhancing the performance of microbial electrolysis cells (MEC) for bio-hydrogen production by optimizing three key parameters: buffer concentration, dilution factor, and applied voltage. The study, conducted at Prince Sattam Bin Abdulaziz University in Al-Kharj, Saudi Arabia, introduces a novel approach by integrating artificial gorilla troops optimization (AGTO) with Adaptive Neuro-Fuzzy Inference System (ANFIS) modelling. This combination aims to determine the optimal values of the parameters to simultaneously increase bio-hydrogen production and energy recovery from MEC. By comparing the results with Response Surface Methodology (RSM) and Artificial Neural Network integrated with Particle Swarm Optimization (ANN-PSO), the new approach showed significant improvements. The RMSE for hydrogen yield model decreased by 91.7% using ANFIS compared to RSM, while the R-square for prediction increased by 5.32%. Similarly, for energy recovery, the RMSE decreased by 91% and the R-square increased by 3.8% using the ANFIS model. The integration between ANFIS and AGTO successfully boosted hydrogen yield from 576.3 mL/g-VS to 843.32 mL/g-VS, leading to a 34.74% improvement in the overall MEC performance. The study highlights the efficiency and accuracy of the new optimization strategy in enhancing bio-hydrogen production efficiency. This innovative research contributes to the advancement of sustainable bioenergy production and showcases the potential of artificial intelligence in optimizing biofuel production processes.