The digitization of the water sector has led to the emergence of Smart Water Metering Networks (SWMNs), which enable automated and continuous water consumption measurement. However, challenges persist in efficiently managing and transmitting the vast amount of data generated by these networks. To address this, researchers have proposed anomaly detection techniques to identify and detect anomalies within SWMNs. In the realm of anomaly detection, a substantial body of research has emerged. However, there is a notable gap in the literature on a comprehensive synthesis of Machine Learning (ML) applications for anomaly detection in SWMNs. To bridge this knowledge gap, this study evaluated thirty-two research papers written between 2016 and 2023, focusing on ML applications for anomaly detection in smart water systems, smart water grids, water distribution networks, and water networks. From an initial pool of 725 research papers, those directly related to ML-based anomaly detection techniques were selected and analysed using both quantitative and qualitative data analysis. The study revealed that ML techniques such as k-Nearest Neighbors (kNN), Autoencoders, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Random Forest, Decision Trees, and Support Vector Machines (SVM) have made significant contributions to anomaly detection within these networks. Notably, researchers frequently employed multiple ML techniques to enhance accuracy. Performance metrics analysis demonstrated that F1 score, precision, accuracy, and recall were commonly used to assess the quality of ML anomaly detection techniques. This review aims to provide researchers with recommendations for selecting suitable ML anomaly detection techniques in SWMNs. The findings underscore the promising nature of ML applications for anomaly detection in SWMNs and highlight the need for further research in this area.