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Abstract

This study focuses on routing optimization process using intelligent technologies such as Machine Learning, Deep Learning, and Deep Reinforcement Learning. With the widely use of Internet and online services, users expect faster, reliable, and uninterrupted network connections; therefore, routing optimization has become essential. The main objectives are to select the best paths for traffic, reduce delays, traffic congestion, increase throughput, and efficiently use network resources. Traditional heuristic and mathematical methods cannot fulfill these criteria, which has facilitated the adoption of intelligent routing optimization techniques. To understand state of the art, we conduct a systematic literature review on intelligent routing optimization. The analysis shows that intelligent routing methods can enhance network throughput, reduce delays and congestion, and provide effecient routing compared to traditional techniques. However, several challenges remain, such as intensive computation, time-consuming training, and ignoring hardware limitations which directly affect performance. Resolving these issues is critical to fulfill user expectations.

Keywords

Intelligent Routing Deep Reinforcement Learning Machine Learning Routing Optimization Traffic Engineering

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Copyright (c) 2026 Kabul University

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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Rahmatzai, S., & Wasi, N. A. (2026). Intelligent Routing Optimization in Dynamic Network Environments: A Systematic Review of Advances and Limitations. Journal of Natural Sciences – Kabul University, 9(1), 211–232. https://doi.org/10.62810/jns.v9i1.563
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