Green hydrogen, produced by renewable energy-powered water electrolysis, is emerging as a promising solution for global decarbonization. Green hydrogen offers a sustainable alternative to fossil fuels, reducing greenhouse gas emissions across various industries. However, several challenges, including energy efficiency, high production costs, and storage concerns, have turned out to be the major impediments to its large-scale deployment. Artificial Intelligence (AI) provides transformative opportunities to optimize green hydrogen production, improve storage techniques, and enable integration with renewable energy systems. This review explores the role of AI techniques, encompassing machine learning, reinforcement learning, and Digital Twins (DTs), in enhancing electrolyzer performance, system degradation prediction, and hydrogen distribution network management. Additional AI applications for storage material discovery, energy forecasting, and sector-level applications in transport, power, and industry are also highlighted in this review. A conceptual framework is provided to map AI models to every stage of the hydrogen value chain. Gaps in the literature and opportunities for interdisciplinary collaboration are identified toward the realization of scalable intelligent hydrogen systems. The review concludes by proposing future avenues for AI-enabled innovations, maintaining a sustainable green hydrogen economy.