Hydrogen storage, as one of the key strategies in the transition to sustainable energy systems, plays a significant role in reducing greenhouse gas emissions. The optimal performance of hydrogen storage reservoirs is influenced by multiple phenomena, including wettability alterations, solubility in saline environments, dispersion processes, viscosity variations, and gas adsorption capacity. In this study, the effects of cushion gases on these processes were investigated, and the complex relationships among the influential variables were analyzed using machine learning methods. The results indicate that the presence of cushion gases in the porous medium has a direct impact on wettability characteristics, including contact angle and interfacial tension, consequently altering the multiphase behavior of fluids within the reservoir. Furthermore, the examination of hydrogen solubility in saline environments reveals that factors such as pressure, temperature, and salinity affect the stability of storage reservoirs, and controlling these variables plays a crucial role in optimizing the storage process. Additionally, the phenomenon of dispersion due to interactions between hydrogen and cushion gases leads to changes in hydrogen purity and a reduction in storage efficiency. Viscosity, as one of the most critical fluid properties, influences the movement of gas within the porous medium. Changes in the composition of cushion gases can cause viscosity fluctuations, affecting the injection and withdrawal performance of hydrogen. Moreover, gas adsorption capacity in storage reservoirs is a function of reservoir rock characteristics, total organic carbon content, and the pressure and temperature conditions of the environment; a precise understanding of these variables can lead to optimization of storage system performance. In this study, machine learning methods were employed to develop analytical models to examine the relationships among these variables, demonstrating that analysi