Water scarcity is one of the main challenges that Gulf Cooperation Council (GCC) countries have been facing for multiple decades.Out of the various solutions that were implemented to face the challenge, the demand and supply side management was heavily investigated. The balance between water supply and demand requires efficient water management system techniques, which is highly dependent on the use of accurate forecasting methodologies and tools. There is no global single optimum method used to forecast the water demand. It is more of a case-by-case approach depending on the network complexity, operational limitations, available data, forecast horizon, intuitiveness of the tool, accepted percentage of deviation between actual and forecasted demand. The purpose of this paper is to achieve the global sustainability goal by addressing the gap that is currently present between water supply and demand. This is done by presenting an innovative accurate methodology to forecast the water demand using machine learning (ML) for short-term. The results for a water utility in United Arab Emirates (UAE) showed that the mean absolute percentage error (MAPE) between the actual and forecasted demand was reduced from 5.42% for the conventional forecasting method to 3.25% for the proposed ML forecasting method. Similarly, the root mean square error (RMSE) was reduced from 11.14 million imperial gallons per day (MIGD) for the conventional forecasting method to 7.6 (MIGD) for the proposed ML forecasting method. Additionally, the total difference per year between the actual and forecasted demand was reduced from 2683 million imperial gallons (MIG) for the conventional forecasting method to 898 (MIG) for the proposed ML forecasting method. This shows that by having an accurate demand forecast, the gap between the actual and forecasted demand can be reduced which will improve the overall efficiency and performance of the water management system.