Harnessing renewable energy sources for hydrogen production using artificial intelligence within a circular economy framework

The alarming scale of devastation in our environment and the grim picture of the global fuel crisis has spurred the need for sustainable energy alternatives. The hydrogen production from renewable energy sources offers a promising solution to achieve a carbon–neutral energy future in line with UN sustainability development goals. This review explores various renewable energy options like solar, wind, geothermal, and biomass for clean hydrogen production, emphasizing their potential, efficiency, life cycle assessment, and recent technological advancements. It also investigates the applications of artificial intelligence and machine learning in hydrogen production, inspecting the associated challenges and the opportunities available for researchers to pursue further investigations. A bibliometric analysis using the Scopus database and VOS viewer identifies research gaps in this field, putting spotlight on areas such as biomass-based hydrogen production and alternative storage methods. The density maps highlight the less predominant renewable sources as biomass, natural gas (yellow cluster) and fuel cells, electric energy storage (purple cluster) indicating gaps which could be explored by future research enthusiasts. This study further highlights the significance of circular economy principles and role of AI in maximizing resource utilization while minimizing waste. While challenges such as high production costs and infrastructure limitations persist, policy support and technological maturity and market conditions are guiding the progress of this technology through public private partnerships. By mapping the current research landscape and identifying key gaps, this review aims to provide a valuable reference for researchers and policymakers to advance sustainable hydrogen production and ensure a smooth transition toward a clean energy economy.