Gas-liquid two-phase flow is seen in various engineering disciplines and understanding of its interfacial structure is a great importance for proper model development. Flow regime maps have been developed by various researchers in the past. However, identification of flow regime involves subjectivity and technical difficulty as the nature of flow regime transition mechanism and criteria are still yet unknown. In the present article, various two-phase flow regime identification methods utilizing machine learning (ML) approach will be reviewed. Two-phase flow features such as two-phase mixture impedance, dynamic force signal, and high-resolution images from high-speed camera were selected for the ML model training and testing. For the machine learning models, artificial neural network (ANN), convolutional neural network (CNN), and convolutional long short-term memory (ConvLSTM) were tested, depending on the feature types. As a result, the ML approach can identify two-phase flow regime with high accuracy. There still remains issues related to ML usage, including hyperparameter selection, and ability to explain its decisions. However, adaptation of ML tools for multiphase flow research fields may provide utmost benefits for efficient signal and image classification approach.