Modeling biohydrogen production from microbial electrolysis cells in the machine learning era: a review

• Biofilm dynamics modeling improves electron transfer efficiency in MECs. • Hybrid AI models optimize MEC energy efficiency and biohydrogen production. • Advanced MEC simulations integrate electrochemical and microbial interactions. • DFT-based catalyst modeling accelerates HER catalyst discovery for MECs. • ML improves MEC process control and operational stability. Hydrogen production through microbial electrolysis cells (MECs) presents a sustainable solution for renewable energy and wastewater treatment, yet commercialization faces challenges in optimization, scalability, and efficiency. This review explores the evolution of MEC modeling, from traditional mathematical frameworks to advanced hybrid approaches integrating machine learning (ML) and artificial intelligence (AI). ML techniques, including ANNs, ANFIS, and deep learning models (e.g., LSTM, Bi-LSTM), enhance predictive accuracy, parameter optimization, and real-time control, with hybrid models achieving up to 267% higher hydrogen production rates. Despite progress, challenges such as the “black-box” nature of ML models, material costs, and data standardization remain. The review emphasizes the need for technoeconomic and life cycle analyses to assess commercial viability and environmental impacts. Future research should focus on multi-scale modeling, IoT-enabled control systems, and cost-effective catalysts to bridge the gap between lab innovations and industrial deployment, advancing MEC technology toward scalable and sustainable biohydrogen production.