• DF-H 2 from wastes framed as a controllable, scalable energy-conversion platform. • Harmonized data landscape and reporting gaps for waste-derived DF-H 2 datasets • ML, soft sensors and digital twins mapped for real-time DF bioreactor control. • TEA/LCA evidence synthesized for standalone and hybrid DF hydrogen pathways. • Roadmap links process intensification, integration (MEC/PF/AD) and sustainability. Dark fermentative biohydrogen (DF-H 2 ) is a promising route to future low-carbon energy, uniquely positioned to convert diverse organic wastes into hydrogen while providing regulated waste-treatment functions. However, translating DF-H 2 from laboratory systems to field-ready applications demands approaches that are both sustainability-constrained and data-driven, integrating high-quality datasets, advanced modelling, and rigorous techno-economic and life-cycle assessments. This review synthesizes recent advances in DF-H 2 from waste streams by linking biochemical pathways, microbial ecology, and reactor engineering with emerging digital tools. It maps the data landscape for waste-derived DF-H 2 , examines kinetic, statistical, and ML-based models, and discusses smart sensing, soft sensors, and digital twins for real-time monitoring and control. The review further consolidates TEA and LCA evidence for stand-alone and hybrid DF configurations, and analyses integrated multi-stage platforms such as DF–MEC, DF–photofermentation and DF–AD in the context of scale-up. Finally, it identifies key scientific, digitalisation, and policy challenges. Thus, this review uniquely couples data-driven process intensification with TEA/LCA-grounded sustainability assessment for waste-derived DF-H 2 , providing an integrated roadmap for field-realization of DF-centric waste biorefineries.
Rambabu et al. (2026) studied this question.