In light of the environmental consequences linked to the burning of fossil fuels, there is a mounting push to
devise cleaner means of energy generation. The production of hydrogen is gaining momentum as a favored
avenue for enabling the shift toward sustainable energy. Effective hydrogen storage is crucial for sustainable
energy solutions, and the inclusion of cushion gas in geological storage could aid in maintaining formation
pressure during hydrogen reproduction while expanding pore volumes for gas by preventing water presence in
pores. Accurately controlling hydrogen behavior and interactions in porous media is crucial for successful
storage, with interfacial tension between gas and water playing a significant role in the trapping and mobilizing
the phases. Thus, developing a predictive tool to simulate interfacial tension measurements is essential. This
study employs various intelligent modeling techniques, including Adaptive Neuro-Fuzzy Inference System,
Multilayer Perceptron optimized with Bayesian Regularization, Levenberg-Marquardt, and Scaled Conjugate
Gradient algorithm, Grey Wolf Optimizer-based Least Squares Boosting (GWO-LSBOOST), GWO-based Radial
Basis Function, GWO-based Least Squares Support Vector Machine, and Extreme Learning Machine (ELM), to
develop interfacial tension models for brine-hydrogen/cushion gas systems. Applying these models developed
based on diverse theories, structures, and performance characteristics helps identify the optimal predictor for the
target parameter. The evaluation of the models was carried out using 2868 experimental data points. The
achieved results demonstrated the satisfactory performance of different modeling techniques (with R2 falling
within the range of 0.8979 to 0.9960), however, GWO-LSBOOST outperformed others (improving R2 from
0.8979 to 0.9960 and AARE% from 5.5714 % to 0.8060 %, compared to ELM). Leverage outlier identification
and trend estimation analysis confirmed the statistical reliability of the dat