Machine learning–driven biohydrogen production: a cross-pathway review of process performance, economics, and sustainability

Biohydrogen production offers a renewable route to convert biomass, organic residues, wastewater, and other biogenic carbon streams into a low-carbon energy carrier through biological, photochemical, electrochemical, and thermochemical pathways. Due to the complex, highly nonlinear nature of these conversion processes, conventional modeling approaches often struggle to capture system behavior across different operational scales and pathways. As a result, machine learning (ML) has emerged as a powerful tool for modeling and optimizing biohydrogen production systems. This review synthesizes ML-assisted biohydrogen production across feedstock–process–model relationships, algorithmic architectures, interpretability methods, optimization strategies, techno-economic performance, and environmental implications. The literature indicates that ML is primarily used as a surrogate modeling tool to predict hydrogen yield, production rate, syngas composition, electrochemical performance, and cost–emission trade-offs, with the greatest methodological focus on biomass gasification and fermentative systems. Across routes, influential descriptors cluster around feedstock chemistry, reaction environment, transport and electrochemical conditions, catalyst structure, irradiation regime, and residence-time effects. Despite their strong apparent predictive performance, current models remain limited by sparse and heterogeneous datasets, route-specific calibration, insufficient external validation, inadequate uncertainty quantification, and poor integration of mechanistic knowledge. Progress will require physics-informed and uncertainty-calibrated ML frameworks linked to active learning, external validation, techno-economic analysis, and life-cycle assessment. Such integration is essential for moving from local prediction toward transferable biohydrogen process design.