The growing energy demand and the need for climate mitigation strategies have spurred interest in the application of CO₂–enhanced oil recovery (CO₂–EOR) and carbon capture, utilization, and storage (CCUS). Furthermore, natural hydrogen (H₂) production and underground hydrogen storage (UHS) in geological media have emerged as promising technologies for cleaner energy and achieving net–zero emissions. However, selecting a suitable geological storage medium is complex, as it depends on the physicochemical and petrophysical characteristics of the host rock. Solubility is a key factor affecting the above–mentioned processes, and it is critical to understand phase distribution and estimating trapping capacities. This paper conducts a succinct review of predictive techniques and present novel simple and non–iterative predictive models for swift and reliable prediction of solubility behaviors in CO₂–brine and H₂–brine systems under varying conditions of pressure, temperature, and salinity (T–P–m salts), which are crucial for many geological and energy–related applications. The proposed models predict CO₂ solubility in CO₂ + H₂O and CO₂ + brine systems containing mixed salts and various single salt systems (Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl⁻, SO₄²⁻) under typical geological conditions (273.15–523.15 K, 0–71 MPa), as well as H₂ solubility in H₂ + H₂O and H₂ + brine systems containing NaCl (273.15–630 K, 0–101 MPa). The proposed models are validated against experimental data, with average absolute errors for CO₂ solubility in pure water and brine ranging between 8.19 and 8.80% and for H₂ solubility in pure water and brine between 4.03 and 9.91%, respectively. These results demonstrate that the models can accurately predict solubility over a wide range of conditions while remaining computationally efficient compared to traditional models. Importantly, the proposed models can reproduce abrupt variations in phase composition during phase transitions and account for the influence of different ions on CO₂ solubility. The solubility models accurately capture the salting–out (SO) characteristics of CO₂ and H₂ gas in various types of salt systems which are consistent with previous studies. The simplified solubility models for CO₂ and H₂ presented in this study offer significant advantages over conventional approaches, including computational efficiency and accuracy across a wide range of geological conditions. The explicit, derivative–continuous nature of these models eliminates the need for iterative algorithms, making them suitable for integration into large–scale multiphase flow simulations. This work contributes to the field by offering reliable tools for modeling solubility in various subsurface energy and environmental–related applications, facilitating their application in energy transition strategies aimed at reducing carbon emissions.

Hydrogen (H2) is expected to become a key component of the global energy mix, both through engineered underground storage and the exploitation of naturally occurring subsurface deposits. A critical parameter governing the feasibility of these strategies is the solubility of H2 in water, which directly influences its reactivity, migration, and storage capacity in porous media. Experimental determination of H2 solubility under reservoir conditions is, however, costly, hazardous, and limited in scope. Here, we present two advanced machine learning models—gradient boosting (GB) and light gradient boosting machine (LightGBM)—trained on a dataset of 545 experimental measurements of H2 solubility in pure water across wide ranges of pressure and temperature. The GB model achieves superior performance (R2 = 0.9974; RMSE = 0.000165 mol mol−1), outperforming LightGBM. Trend analysis confirms that the models capture the underlying thermodynamic behavior of H2 solubility with respect to temperature and pressure, while leverage analysis using William’s plot indicates that fewer than 2% of the data points fall outside the model’s applicability domain. These results demonstrate that boosting-based algorithms can deliver highly accurate, rapid, and cost-effective predictions of H2 solubility, offering a robust alternative to conventional laboratory measurements. The validated GB model provides a practical tool for assessing hydrogen reactivity in geological systems and has immediate implications for the design and optimization of underground hydrogen storage and natural hydrogen production.