Smart Water Metering Networks (SWMNs) stand as pivotal infrastructure, crucial for communities and industries. The escalating value of water resources due to climate change and overexploitation underscores the urgency of optimizing these networks for efficiency and resilience. This study focuses on identifying anomalies within SWMNs to address challenges impeding efficient water resource management. Leveraging a comprehensive 72-month dataset from Windhoek, Namibia, this research employs a meticulous analytical approach to unveil diverse anomaly types prevalent within SWMNs. Anomalies, including irregular consumption patterns, leakages, and inaccurate meters, contribute significantly to both apparent and real losses. By scrutinizing this dataset, the study reveals nuanced anomaly patterns like persistent zero consumption and unexpected fluctuations, highlighting the pervasive nature of these issues within the network. The findings not only shed light on these multifaceted anomalies but also lay the groundwork for future advancements in machine learning-based anomaly detection techniques. This research holds promise beyond academia, offering practical implications for water utility management. Identifying and understanding these anomalies serves as a stepping stone toward developing robust detection systems, ultimately fostering heightened efficiency and resilience in water networks. This study serves as a catalyst for strategic improvements, enabling more sustainable and efficient utilization of water resources amidst evolving environmental challenges.