Machine learning for hydrogen technologies: A comprehensive review of challenges, opportunities, and emerging trends

Hydrogen technologies are central to the transition toward more sustainable energy systems. Machine learning (ML) and artificial intelligence (AI) are emerging as key enablers across the hydrogen value chain, from production and transport to storage and utilization. This review synthesizes ML applications across these domains, mapping hydrogen technologies to ML task types and method families and assessing reported benefits and limitations. A systematic review of 314 peer-reviewed studies (2019–2024) shows rapid growth in ML-driven research, concentrated in production and material characterization, with transport, underground storage, and several utilization topics still underexplored. Predictive modeling dominates, typically using neural networks and tree-based ensembles, while optimization and hybrid physics-informed approaches are less common. The review highlights gaps in data availability, transparency, and explainability, and outlines priorities for standardized data reporting, adoption of physics-informed and explainable ML, and more rigorous cross-domain benchmarking. • Maps 314 ML studies across hydrogen production, storage, and use. • Reveals dominance of predictive modeling and neural networks. • Identifies research gaps in transport, underground storage, and safety. • Shows limited uptake of explainable, hybrid, and physics-informed ML. • Recommends data sharing, benchmarks, and transparent reporting practices.