The correct equipment operation in the Oil and Gas industry is essential to reduce environmental, human, and financial losses. In this scenario, dry gas seals (DGS) of centrifugal compressors were studied, as they are identified as the most critical device due to the extent of the potential damage caused by their failure. In this study, 31 regression models available at Scikit-Learn were developed using machine learning (ML) techniques. They were trained with a scarce dataset, created based on a design of experiment technique, to replace numerical simulations in predicting the operational reliability of DGSs. First, a model based on Computational Fluid Dynamics (CFD) simulation was validated to represent the gas flowing between the sealing faces, to enable the calculation of the equipment’s operational reliability. Thus, the open-source CFD software OpenFOAM was used together with the substance database of the software REFPROP, to allow the user to define the gas mixture and the evaluated operational conditions. Then, two case studies were carried out following a proposed generic workflow. The first comprised determining a regression model to estimate the reliability of a DGS whose mixture composition (composed of methane, ethane, and octane) is fixed but its operating conditions can vary. The second consisted of determining a more robust regressive model, where both the mixture composition and the operational conditions can vary. Finally, the feasibility of implementing both models under realistic operating conditions was evaluated, based on the infinity norm obtained for the prediction of the test set. The performances achieved were 1.872 degrees Celsius and 6.951 degrees Celsius for the first and second case studies, respectively.