With the development of the Internet of Things, 5G/6G, artificial intelligence and distributed computing technology, edge computing has become a core service paradigm with its advantages of low latency and high bandwidth. However, it faces challenges such as task explosion, heterogeneous resources, and uneven geographical distribution, resulting in insufficient bandwidth prediction accuracy, high scheduling costs, difficulty in unloading decisions, and load imbalance. Existing research has limitations in multi-scale feature capture, multi-objective optimization, and heterogeneous resource adaptation. This study constructs a full chain intelligent optimization framework: the Fre iTT model is used to capture the periodic characteristics of bandwidth and fuse spatiotemporal dependencies, reducing temporal prediction errors by 4.46% to 88.39%; Design HLSPSO algorithm to optimize the bandwidth cost of transmission tasks; By jointly optimizing the energy consumption, latency, and load balancing of computing tasks through TOPPO algorithm, the performance is improved by 8.36% to 21.75% compared to six DRL algorithms; Develop Rainbow LBO algorithm to reduce load imbalance by 3.10% to 23.43%. The results have been applied to actual systems, promoting the implementation of edge computing in industrial intelligent networks, AIGC and other scenarios. In the future, it is necessary to explore cross layer resource collaboration, spatio-temporal adaptation of task scheduling, and federated learning privacy protection to promote edge computing to be more intelligent, efficient, and secure.

Hangjie Zheng. Research on Edge Computing Network Task Scheduling and Resource Management Optimization Based on Artificial Intelligence Technology. International Journal of Big Data Intelligent Technology (2026), Vol. 7, Issue 1: 35-44. https://doi.org/10.38007/IJBDIT.2026.070105.

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