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In today’s rapidly advancing landscape of information and communication technologies, metropolitan areas face a pressing challenge: water shortages combined with the urgent need for smart, sustainable water distribution systems. Deploying an Internet of Things (IoT)-driven monitoring solution can substantially strengthen oversight of water supply networks and help address common distribution problems such as leaks, pipe bursts, unauthorized withdrawals, tank overflows, and pump dry-running. Nevertheless, the proliferation of sensors and communication endpoints across smart urban environments is placing considerable strain on conventional networks, leading to higher latency and greater power consumption. This research outlines several practical approaches to minimize both delay and energy consumption in IoT-supported water distribution infrastructure in intelligent cities. A range of IoT-compatible network topologies is introduced, each shaped by specific hydraulic design features, surface land cover characteristics, and prevailing wireless propagation conditions. After assessing these layout options together with all critical communication metrics, the most suitable network structure for ongoing surveillance of a municipal water supply system in an Indian metropolitan setting is selected. As a practical illustration, the study develops and evaluates an IoT architecture tailored to a secondary-tier metropolitan city in India—namely, Kochi. Using location-specific conditions and the prevailing land-use/land-cover distribution, detailed delay and energy performance models for the IoT-enabled distribution network are constructed and examined. The paper also describes algorithms for classifying nodes and assigning tasks across edge and fog layers, supported by quantitative assessments of the resulting delays and energy consumption. These models yield approximate values for overall network latency and power consumption. Drawing on the derived outcomes and the associated state transition representations, the optimal positioning of fog computing nodes relative to edge devices and the central cloud platform can be determined. Moreover, evaluations across multiple configurations show that increasing the number of operational states in the transition diagram can deliver energy savings of up to 40%. The proposed techniques offer a roadmap for realizing low-latency, energy-conscious IoT communication infrastructures customized to particular geographic and functional contexts.