Based on the experimental training of the simulated water supply pipeline network and the model verification in actual water balance tests, it can be concluded that the constructed neural network-based water supply network status model exhibits a high level of accuracy and holds a certain practical value. This article explores a method that utilizes the IoT to rapidly acquire a large amount of metering data from water supply networks and employs neural network pattern recognition to evaluate water leakage and metering discrepancies. It effectively addresses the limitations of existing evaluation methods, which lack efficiency and accuracy, and provides an effective means of assessing the health status of water supply networks. Future considerations include the adoption of big data technology for automatic training on different network topologies. Additionally, the inclusion of more complex neurons for analyzing and estimating is envisioned, with the ultimate goal of establishing an evaluation and analysis system that is adaptable to all water supply networks.