In the healthcare industry, the integration of the Internet of Things (IoT) and cloud computing (CC) enables access and sharing of worldwide health datasets. Thus, it solves complex problems like data security, privacy, and storage. However, the wide usage of cloud infrastructure increases traffic and reduces cloud performance. Hence, in this article, a novel hybrid Honey Pot-based Feed Feedback Neural System (HPbFFNS) framework was proposed to allocate resources optimally to medical applications (tasks). This framework incorporates the features of Honey pot optimization, and Feed Feedback Neural Network (FFNN). Initially, the health information of patients is collected using the IoT devices and forwarded into the gateway layer for further processing. The task scheduler in the gateway layer analyzes the resource availability, deadline, and priority of the incoming requests to reduce the response time, and waiting time. The honey pot fitness function in the resource allocator helps to allocate resources optimally to the tasks. Additionally, for verification purposes, the results are contrasted with those of current techniques. The experimental and comparative analysis confirms that the suggested model outperforms the traditional algorithms in terms of response time, energy consumption, and resource utilization.