Metaheuristic Optimization Review MOR 3066-280X 10.54216/MOR https://www.americaspg.com/journals/show/3489 2024 2024 Electrical Engineering Department, College of Engineering, Taif University, Taif 21944, Saudi Arabia Sherif. Sherif. This review aims to identify metaheuristic optimization and machine learning in the context of network management in the current era and some graphs of real network applications, such as traffic prediction, resource assignment, and network protection. Bio-inspired meta-functions, which model heuristic approaches to problem-solving in nature, have been shown to provide the best solutions to the OP problem and possess properties that make them ideal for optimizing dynamic networks. In the same vein, neural networks and reinforcement learning models have also performed significantly better in optimizing network performance by providing precise forecasts and decision-making adaptabilities. Incorporating these methodologies into folded working models has facilitated the development of solutions for the more complicated new networks such as SDNs, MANETs and IoTs. This review consolidates the most recent work in this field while identifying new advances as revolutionary technologies for refining the next-generation networks; it discusses possible paths for future research to overcome the existing drawbacks. 2025 2025 01 10 10.54216/MOR.030201 https://www.americaspg.com/articleinfo/41/show/3489