Green hydrogen emerges as a critical solution in the global transition to sustainable energy, yet its widespread adoption remains constrained by complex technological and economic challenges. This was a comprehensive review where the authors addressed the transformational possibilities of using robust artificial intelligence (AI) approaches in green hydrogen production, while providing a critical evaluation of the state-of-the-art protocols that have the potential of alleviating existing system bottlenecks. AI supersedes the performance gap by combining trials-free methods involving artificial neural networks, complex geospatial analysis, and multi-objective optimization algorithms across these three core challenges: dramatically enhancing energy conversion efficiency, significantly lowering production costs, and accurately pinpointing ideal geographical location and sites for hydrogen generation. Through a systematic examination of recent scientific literature, this study reveals how AI-driven approaches are not merely incremental improvements but potentially paradigm-shifting innovations that could accelerate the economic viability and scalability of green hydrogen technologies. Beyond technical achievements, the research critically evaluates persistent obstacles including data scarcity, computational complexity, and model interpretability while offering a nuanced perspective on the strategic research directions that could transform these challenges into opportunities for breakthrough technological development.