AbstractWater distribution network (WDN) leakage is estimated to account for more than 20% of potable water in the United States. Because existing leakage detection practices are mainly laborious or require excessive budget allocation, novel model-based techniques have become prevalent by employing smart meters. Specifically, incorporating machine learning and metaheuristic optimization methods into such model-based approaches to predict water pipeline leakage is one of the state-of-the-art approaches. This paper presents a predictive leakage detection approach by leveraging advanced metering infrastructure (AMI) and cyber-monitoring data. Primarily, the synchronous collection of consumption and monitoring data (i.e., flow and pressure) constitutes the proposed predictive tool in a combinative framework of genetic algorithms (GA) and particle swarm optimization (PSO). The time-consuming EPANET 2.2 simulation toolkit in MATLAB is circumvented by a set of trained artificial neural networks (ANNs). Sensitivity analyses are conducted to investigate the sensitivity of the proposed leakage prediction approach to multiple parameters. Ultimately, the number and location of the flow and pressure monitoring stations are optimized using maximum sensor availability thresholds. Mean absolute percentage error (MAPE) is employed to measure the model’s accuracy. The proposed predictive tool offers insightful horizons on how considerable background leakage can be detected and prioritized without using labor-intensive, costly methods.