Hydrogen, hailed as an environmentally friendly fuel, is gaining prominence as a clean energy carrier. The article delves into the synergy between renewable energy sources (RESs) and hydrogen production, emphasizing cost reduction and efficiency. Despite RESs' intermittency, the potential of using surplus electricity for hydrogen conversion and storage is highlighted. Geological structures like salt caverns and rock salt deposits are identified as viable options for large-scale hydrogen storage. The article underscores the significance of robust storage solutions to complement renewable energy systems. Artificial intelligence, specifically machine learning algorithms, plays a pivotal role in simulating complex phenomena related to underground hydrogen storage. Studies confirm the efficacy of machine learning in predicting crucial parameters affecting storage capacity. The integration of machine learning with Geographic Information Systems (GIS) revolutionizes site selection for underground hydrogen storage by providing detailed suitability maps. The research introduces a novel AI framework comprising multiple algorithms to identify optimal locations for underground hydrogen storage within rock salt formations in Poland. The methodology combines AI, Multi-Criteria Decision Analysis (MCDA), and GIS spatial analysis to enhance accuracy in determining storage potential, potentially transforming decision-making processes. The study focuses on the Na1 rock salt deposit in southwest Poland, showcasing the favorable attributes of the location for hydrogen storage. Overall, the article showcases the positive impact of AI and GIS integration in advancing hydrogen storage solutions.