The article discusses the significance of hydrogen in the global transition towards cleaner energy sources, focusing on Underground Hydrogen Storage (UHS) in saline aquifers. Efficient storage of hydrogen is crucial for balancing energy supply and demand and integrating renewable energy sources. The discussion revolves around the management of interfacial tension (IFT) between hydrogen and brine within geological formations, impacting storage efficiency. Various factors like pressure, temperature, salinity, and gas impurities influence IFT, affecting key aspects of hydrogen storage such as injectivity and long-term stability. The article emphasizes the importance of accurate IFT estimation for effective hydrogen storage design. It also highlights the limitations of conventional laboratory testing methods and the potential of Machine Learning (ML) models to provide fast, accurate predictions for hydrogen–brine systems. ML models offer a data-driven approach to overcome experimental challenges and enhance predictive accuracy. The integration of hybrid and ensemble ML methods has shown promising results in predicting IFT and optimizing operational strategies in various applications. The article underscores the critical role of ML in advancing underground hydrogen storage technologies and improving efficiency in energy distribution. Overall, the focus on enhancing interfacial tension management through ML models reflects a positive outlook on the future of efficient hydrogen storage and the broader shift towards a low-carbon hydrogen economy.