As a promising green energy carrier, hydrogen requires efficient storage solutions. Metal-Organic Frameworks (MOFs) have gained significant attention due to their ultra-high specific surface area, tunable porosity, and the capability for molecular, scale structural engineering. This review critically examines the evolution of MOF design, transitioning from molecular engineering to intelligent modeling. In this research, the interplay between synthesis parameters and hydrogen adsorption performance is analyzed, and the effectiveness of computational methods, specifically Density Functional Theory (DFT) and Grand Canonical Monte Carlo (GCMC) simulations, in predicting material performance is evaluated. Furthermore, the transformative role of machine learning in accelerating the discovery of optimized materials is explored. Finally, key challenges in transitioning from laboratory to industrial scales, including stability under real, world conditions, volumetric density, and thermal management, are discussed, and a roadmap for future research aimed at the commercialization of this technology is provided.