Abstract: A smart water management system is proposed in this paper to identify leakages and predict the location of leakages in pipelines. The system determines leakages by utilizing the flow rates of water in pipelines and predicts the location of the leakages by applying machine learning (ML) techniques. To predict the location of the leakages in the pipeline, different ML approaches have been developed and tested. A comparison of these models is performed to obtain the best model for location prediction. A prototype has been developed in STAR-CCM+, a Computational Fluid Dynamics (CFD) software, to test the proposed system. The results show that amongst the machine learning based location prediction models, the Multi-Layer Perceptron (MLP) performs the best with an accuracy of 94.47% and an F1 score of 0.95.