<![CDATA[This study aims to compile an integrative review of Smart Water Management Systems architecture in urban contexts to identify technology integration patterns, dominant methodological approaches, and recurring research gaps. The method used is an Integrative Literature Review of 16 national and international journal articles indexed in Scopus. The review was conducted through systematic selection, structured data extraction, and comparative-thematic synthesis to map architectural components, analytical models, optimization strategies, and evaluation frameworks. Results show that system architecture evolves in a layered structure integrating IoT for data acquisition, wireless communication networks for connectivity, cloud-edge computing for data processing, and AI/ML for predictive analytics and automated decision-making. Optimization strategies focus on water distribution efficiency, improved operational reliability, and resource loss reduction. The literature also reveals gaps in system interoperability, data standardization, cybersecurity, AI model transparency, and the lack of comprehensive evaluation dimensions. The study concludes that architectural integration and systemic evaluation are essential for developing adaptive, reliable, and sustainable urban Smart Water Management Systems.]]>
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