Review of Emerging AI Techniques for Green Hydrogen in Sustainable Energy

Abstract

Green hydrogen is sustainable energy and clean source that has the potential to significantly alter important industries like manufacturing, the production of electricity, and transportation. Green hydrogen has the potential to ease the transition to low-carbon energy sources because of its leading role in global decarbonization initiatives. Increasing manufacturing productivity, ensuring the stability of storage, and coordinating usage strategies with commercial scalability remain significant challenges This review examines the application of artificial intelligence (AI) to these pressing issues throughout the green hydrogen value chain. In particular, it examines how optimization, deep learning, and machine learning (ML) can boost electrolyzer efficiency in fuel cell applications, improve energy conversion processes, forecast system performance, and optimize storage options. In addition, it is shown how AI-driven real-time monitoring systems and digital twins can be integrated into hydrogen infrastructures, showing how these tools make adaptive operational control and predictive maintenance easier. This article uses current technology advancements and a few selected case studies to critically examine how AI accelerates innovation in green hydrogen technologies in order to facilitate the development of affordable, scalable, and sustainable energy systems of the next generation.