Forecasting water consumption from administrative records is fundamental to the sustainable management of water resources. Analyzing data like billing, historical usage, and user features allows us to identify consumption patterns, project future water needs across various sectors, and estimate the spatial distribution of the population. This method establishes a crucial connection between water usage and urban development trends. To execute this, an exploratory analysis of historical water consumption data in Quito city was conducted using records from the local water management authority. Subsequently, statistical and machine learning methods were applied to forecast the spatial distribution of the population. These methods provide planners with more precise and realistic estimates. However, the process faces key challenges. The quality and timeliness of administrative data can be an issue due to the manual collection process. Furthermore, the reliability of the population distribution estimates is directly dependent on the spatial accuracy of water meter locations and the correct classification of user types. In conclusion, leveraging water consumption forecasting as a driver for population distribution estimation is a valuable strategy. It strengthens water management, promotes sustainability, and facilitates more effective, evidence-based decision-making.