This study uses an efficient transmission model based on the Hybrid Meta-heuristic Model to enhance data transfer by reducing time complexity. Initially, data is moved into the Complex Event Processing (CEP), which is positioned between the fog layer and the IoT layer. In edge IoT devices, complex event processing comprises real-time analysis, correlation, and interpretation of continuous data streams generated by sensors and edge devices. It seeks to identify significant trends or intricate occurrences in various data streams in order to facilitate quick decisions or immediate reactions. After CEP, a multi-tier priority queue-based model is used to attain priority-aware task scheduling. After the arrival of all the tasks, each task is sorted into slots based on its category. High-priority tasks are completed first due to their preference over lower-priority slots. A software-defined network's optimal resource utilization and task response time are guaranteed by an effective load-balancing method called Hybrid Pigeon Cat Search Optimization Algorithms (HPC_SOA). Arranging tasks based on their availability, capacity, proximity, and energy efficiency may optimize the fog nodes' resource utilization and energy usage. In the evaluation, the proposed approach has consumed 22051 Kw/h of energy.