Hydrogen (H₂) underground storage has attracted considerable attention as a potentially efficient strategy for the large-scale storage of H₂. Nevertheless, successful execution and long-term storage and withdrawal of H₂ necessitate a thorough understanding of the physical and chemical properties of H₂ in contact with the resident fluids. As capillary forces control H₂ migration and trapping in a subsurface environment, quantifying the interfacial tension (IFT) between H₂ and the resident fluids in the subsurface is important. In this study, molecular dynamics (MD) simulation was employed to develop a data set for the IFT of H₂-brine systems under a wide range of thermodynamic conditions (298-373 K temperatures and 1-30 MPa pressures) and NaCl salinities (0-5.02 mol·kg^-1). For the first time to our knowledge, a comprehensive assessment was carried out to introduce the most accurate force field combination for H₂-brine systems in predicting interfacial properties with an absolute relative deviation (ARD) of less than 3% compared with the experimental data. In addition, the effect of the cation type was investigated for brines containing NaCl, KCl, CaCl₂, and MgCl₂. Our results show that H₂-brine IFT decreases with increasing temperature under any pressure condition, while higher NaCl salinity increases the IFT. A slight decrease in IFT occurs when the pressure increases. Under the impact of cation type, Ca²⁺ can increase IFT values more than others, i.e., up to 12% with respect to KCl. In the last step, the predicted IFT data set was used to provide a reliable correlation using machine learning (ML). Three white-box ML approaches of the group method of data handling (GMDH), gene expression programming (GEP), and genetic programming (GP) were applied. GP demonstrates the most accurate correlation with a coefficient of determination (R²) and absolute average relative deviation (AARD) of 0.9783 and 0.9767%, respectively.