Smart city Internet of Things (IoT) deployments consist of thousands of resource-constrained sensor nodes operating under strict energy budgets. Conventional routing protocols fail to balance energy efficiency, scalability, and dynamic adaptability under dense urban traffic conditions. This paper proposes a Hybrid Heuristic Artificial Intelligence (HHAI) based energy-efficient routing framework designed specifically for smart city IoT networks. The proposed method integrates heuristic cluster formation with reinforcement learning-based route optimization and adaptive energy-aware path selection. A hybrid decision metric combining residual energy, link quality, congestion index, and hop count is introduced to dynamically select optimal routes. Simulation results demonstrate significant improvements in network lifetime, packet delivery ratio, and energy consumption compared to conventional LEACH, AODV, and PSO-based routing approaches. The proposed framework enhances scalability and ensures sustainable IoT operation in smart city environments.
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