The rapid evolution of Internet of Things (IoT) networks in smart towns has brought a demand for adaptive and efficient data processing strategies. These networks, which encompass interconnected devices accumulating and transmitting data, face precise challenges, together with dynamic environments, restricted assets, and the need for real-time selection-making. Deep studying algorithms have emerged as powerful gear to cope with these demanding situations, however they require version to suit the precise constraints and needs of IoT networks. This paper proposes a framework for adaptive deep mastering algorithms especially designed for IoT-enabled clever town networks. The framework makes a specialty of optimizing resource allocation, enhancing statistics transmission efficiency, and enhancing the responsiveness of clever city programs in real time. Key additives of this technique include dynamic version choice primarily based on statistics visitor patterns, useful resource-conscious version deployment that balances accuracy and computational necessities, and federated learning strategies to aid statistics privacy and decentralization. Simulation effects display that the proposed framework outperforms traditional fashions in terms of strength performance, records processing speed, and adaptableness to changing network conditions. This paintings contributes to the development of scalable, wise structures which could force innovation in clever towns with the aid of allowing IoT networks to method massive-scale information correctly and autonomously, fostering improved city management and choice-making abilities.