Automobile engines that run on gasoline and diesel are being phased out in favour of electric-powered engines, which are greener, maintenance-free, and silent. Hydrogen is considered as a potential energy source for automobile applications since it has a high gravimetric energy density of roughly 120 kJ/g. However, it has a low volumetric energy density due to its low mass density. As a result, hydrogen storage has been a challenge for the vehicle industry. Toyota and Honda have developed hydrogen-powered cars in which hydrogen is compressed and stored at high pressures of around 700 bar. This actually increases the vehicle's weight and expense. Moreover, storing the hydrogen at such high pressures in the automobile pose safety risks. Therefore, a safer, light-weight and affordable version of fuel-cell EV have to be devised to store hydrogen at reduced pressures. This work aims to increase the hydrogen gas uptake in the fuel cell using porous Metal-Organic Frameworks (MOFs), which physically adsorb the hydrogen gas on their surface, at lower pressures and cryogenic temperature. MOFs are materials made up of a combination of metallic ligands and organic linkers, having high porosity and specific surface area. They are synthesized via solvothermal method in the laboratory and used for hydrogen storage studies. Core-shell MOFs HKUST-1@Cu-MOF-2 (HM) and Cu-MOF-2@HKUST-1 (MH) and their graphene nanoplatelets (GNP) incorporated composites GNP@HKUST-1@Cu-MOF-2 (GHM) and GNP@Cu-MOF-2@HKUST-1 (GMH), GNP@Cu-MOF-2@MOF-5 (GCM), Cu-MOF-2@MOF-5 C@M and interpenetrating MOF T2M were used in this work. A Sievert’s apparatus is used for hydrogen adsorption studies at a range of pressures and temperatures. The number of moles of hydrogen gas adsorbed by the MOF is measured from the corresponding change in gas pressure in the adsorption chamber; the gravimetric (kg-H2/kg) and volumetric storage (kg-H2/L) capacities are calculated. BET analysis is carried out to determine the surface area, pore size, pore-volume, pore size distribution, gravimetric and volumetric capacities of the MOF. As per the target set by the Department of Energy (DOE), USA, gravimetric and volumetric hydrogen storage capacities are 7.5wt% and 70g/L, respectively. The structural data of various MOFs available on HyMARC database are collected and various machine learning algorithms are employed in order to optimize hydrogen storage capacity. In addition, the critical features that maximize the hydrogen uptake in these MOFs are also identified. In the initial work, a Genetic Algorithm (GA) with an evolutionary algorithm is used as an unconstrained optimization method to obtain the optimized pore size for maximum adsorption of hydrogen for storage purposes. An optimum gravimetric storage capacity of ~12% is obtained at a pressure swing of 5-100 bar and a constant temperature of 100 K, along with the corresponding feature values.