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Edge computing enables even low-powered devices to move part of their processing work to the edges of the network, improving service quality for everyone. Though several studies have hinted that offloaded work's data volume might affect resource allocation, no comprehensive study has been undertaken on the issue. In this research, a new optical communication strategy for 5G networks is presented, one that makes use of a machine learning-based routing architecture for channel allocation and job scheduling. Within HDFS (Hadoop data file system) environment for Hadoop processing, the header controls the stimulated control. Edge fog networks communicate through optical tier networks, and resources are managed by a service reinforcement heterogeneous multi-objective architecture. In order to schedule jobs, a multi-node delay proximal strategy is used. This policy uses genetically competing objectives. Throughout the course of the experiment, data on things like throughput, delivery rate, efficiency, end-to-end latency, and collision probability will be collected and analyzed. By implementing the plan, we decreased the probability of a collision by 65%, increased efficiency by 95%, decreased latency to all nodes by 55%, increased throughput by 98%, and increased the delivery rate by 92%. [ABSTRACT FROM AUTHOR]