Flow pattern is an important engineering design factor in two-phase flow in the chemical, nuclear, and energy industries, given its effects on pressure drop, holdup, and heat and mass transfer. The prediction of two-phase flow patterns through phenomenological models is widely used in both industry and academy. In contrast, as more experimental data become available for gas–liquid flow in pipes, the use of data-driven models to predict flow pattern transition, such as machine learning, has become more reliable. This type of heuristic modeling has a high demand for experimental data, which may not be available in some industrial applications. As a consequence, it may fail to deliver a sufficiently generalized transition prediction. Incorporation of physics in machine learning is being proposed as an alternative to improve prediction and also to reduce the demand for experimental data. This paper evaluates the use of hybrid-physics-data machine learning to predict gas–liquid flow pattern transition in pipes. Random forest and artificial neural network are the chosen tools. A database of experiments available in the open literature was collected and is shared in this work. The performance of the proposed hybrid model is compared with phenomenological and data-driven machine learning models through confusion matrices and graphics. The results show improvement in prediction performance even with a low amount of data for training. The study also suggests that graphical comparison of flow pattern transition boundaries provides better understanding of the performance of the models than the traditional metrics.