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In recent years, health applications based on the internet of medical things have exploded in popularity in smart cities (IoMT). Many real‐time systems help both patients and professionals by allowing remote data access and appropriate responses. The major research problems include making timely medical judgments and efficiently managing massive data utilising IoT‐based resources. Furthermore, in many proposed solutions, the dispersed nature of data processing openly raises the risk of information leakage and compromises network integrity. Medical sensors are burdened by such solutions, which reduce the stability of real‐time transmission systems. As a result, this study provides a machine‐learning approach with SDN‐enabled security to forecast network resource usage and enhance sensor data delivery. With a low administration cost, the software define network (SDN) design allows the network to …