AI-Based Network Traffic Management in Next-Generation Mobile Networks
Author(s): Seema Rani
Publication #: 2601002
Date of Publication: 15.02.2026
Country: India
Pages: 1-4
Published In: Volume 12 Issue 1 February-2026
Abstract
The rapid growth of mobile data traffic driven by video streaming, cloud services, Internet of Things (IoT), and emerging immersive applications has placed unprecedented demands on mobile networks. Fifth Generation (5G) networks and future Sixth Generation (6G) systems aim to provide ultra-high data rates, ultra-low latency, and massive connectivity. However, managing highly dynamic and heterogeneous traffic in such networks using traditional rule-based techniques has become increasingly difficult. Artificial Intelligence (AI), particularly Machine Learning (ML), offers powerful tools to analyze large volumes of network data, predict traffic behavior, and make intelligent resource management decisions in real time. This paper presents a simple and comprehensive study of AI-based network traffic management in next-generation mobile networks. It first explains the characteristics and challenges of traffic in 5G and 6G systems. Then, it introduces key AI and ML techniques relevant to traffic management. Major application areas such as traffic prediction, congestion control, load balancing, network slicing, and quality of service assurance are discussed in detail. The paper also highlights performance metrics, implementation challenges, and future research directions. The study concludes that AI-driven traffic management is essential for achieving efficient, scalable, and reliable operation of future mobile networks.
Keywords: Artificial Intelligence, Network Traffic Management, 5G, 6G, Machine Learning, Network Slicing, Congestion Control.
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