The CO(2)brine interfacial tension (IFT) is a key parameter affecting the CO2 storage capacity in saline aquifers and therefore should be accurately characterized to ensure the optimal design of CO2 sequestration projects. This paper proposed the use of the extreme gradient boosting (XGBoost) trees for the fast and accurate modeling of the CO(2)brine IFT. Results show that the novel model is capable of not only estimating the IFT but also reproducing the underlying correlation between the IFT and each input variable with remarkably high accuracies. Statistical matrices and point-wise error analyses demonstrate that the new model outperforms previous machine learning (ML) methods significantly. The estimation model was then applied for determining the optimum CO2 sequestration depth in saline aquifers, which reveals that higher pressure and/or lower geothermal gradients result in a significant increase in the maximum structural trapping capacity that occurs at noticeably shallower formations.