The pursuit of renewable energy resources and efficient storage solutions has led to the exploration of hydrogen storage in underground reservoirs as a promising alternative to traditional methods. This approach offers vast storage volumes, long-term stability, and a bridge between energy supply and demand, contributing to a sustainable energy future. Subsurface geological features like saline aquifers and depleted oil reservoirs exhibit characteristics suitable for effective hydrogen storage. Accurate estimation of hydrogen losses and solubility behavior in reservoirs is crucial, and experimental studies along with machine learning methods are utilized for precise analysis. Intelligent models like ANFIS and LSSVM optimized with PSO, GA, or CSA are developed to predict solubility of hydrogen and methane mixtures in underground brine reservoirs. These models are based on effective parameters such as temperature, pressure, salt concentration, and hydrogen mole fraction, derived from an accurate experimental dataset. The authenticity of the dataset is ensured through outlier detection algorithms, and sensitivity analysis is conducted to understand how solubility behavior changes with different parameters. Overall, the advancement in artificial intelligence-based models for predicting hydrogen storage behavior in underground reservoirs marks a significant step towards enhancing the safety, security, and efficiency of sustainable energy solutions.