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The solution of how to accurately and timely predict the flowrate of gas–liquid mixtures is the key to help petroleum and other related industries to reduce costs, improve efficiency, and optimize management. Although numerous studies have been carried out over the past decades, the problem is still significantly challenging due to the complexity of multiphase flows. This paper attempts to seek new possibilities for multiphase flow measurement and novel application scenarios for state-of-the-art machine learning (ML) techniques. Convolutional neural networks (CNNs) are applied to predict the flowrate of multiphase flows for the first time and can achieve promising performance. In addition, considering the difference between data distributions of training and testing samples and its negative impact on prediction accuracy of the CNN models on testing samples, we propose flow adversarial networks (FANs) that can distill both domain-invariant and flowrate-discriminative features from the raw input. The method is evaluated on dynamic experimental data of different multiphase flows on different flow conditions and operating environments. The experimental results demonstrate that FANs can effectively prevent the accuracy degradation caused by the gap between training and testing samples and have better performance than state-of-the-art approaches in the flowrate prediction field. [ABSTRACT FROM AUTHOR]