Water scarcity in Mexico City has become an increasingly urgent issue, exacerbated by inefficient and unequal consumption patterns across its urban fabric. This study advances Geographic Information Systems (GIS) research by developing and applying an integrative spatial analysis framework specifically tailored to the complexities of urban water management. Beyond its application to Mexico City, the research demonstrates how GIS can be used to fuse heterogeneous datasets, including those from SACMEX (Mexico City’s Water System), INEGI (National Institute of Statistics and Geography), and DENUE (National Directory of Economic Units), into a unified analytical environment. Through a combination of exploratory data analysis (EDA), spatial data mining, and clustering techniques, the study identifies critical disparities in water consumption at multiple spatial scales, from boroughs to neighborhoods. A key contribution is the implementation of a layered system architecture for managing historic spatiotemporal data, enabling dynamic visualization of consumption patterns. The findings reveal that socio-economic and demographic variables play a decisive role in shaping spatial water demand, with marginalized communities facing disproportionate challenges. While previous spatiotemporal analyses of water consumption in Mexico City have primarily focused on aggregated borough-level data or isolated socio-demographic correlations, they have often lacked multiscale integration, high-resolution neighborhood-level analysis, or interactive visualization tools to support policy development. This research addresses these limitations by providing a fine-grained, multilayered analytical approach that enhances the scientific understanding of urban water use. Beyond offering immediate policy-relevant insights for Mexico City, the methodological framework proposed here contributes to GIS research by providing a scalable, transferable approach for analyzing urban resource consumption patterns. Future work will focus on incorporating real-time data streams, expanding sector-specific analysis, and integrating additional variables and domains required for a comprehensive understanding of water dynamics. This study represents a first step toward building an adaptive, equitable, and efficient urban water management strategy.