Machine Learning Approaches for Intelligent Network Traffic Management in 6G

Year : 2026 | Volume : 16 | Issue : 02 | Page : 19 32
By

Jyoti Amitkumar Dhamecha,

Bhavisha Vishalbhai Parvadiya,

  1. Assistant Professor, Department of Computer Science, Sardar Patel College of Administration and Management, Gujarat, India
  2. Assistant Professor, Department of Computer Science, Sardar Patel College of Administration and Management, Gujarat, India

Abstract

The increasing adoption of smart devices, Internet of Things (IoT) technologies, cloud-based services, autonomous applications, and multimedia platforms has greatly enhanced the demands and complexity of modern wireless communication networks. To meet these evolving requirements, upcoming sixth-generation (6G) communication systems are anticipated to deliver extremely high data transmission speeds, support a vast number of connected devices, ensure minimal communication delays, enable intelligent network management, and provide seamless real-time connectivity for advanced applications. Traditional network traffic management techniques are unable to efficiently handle the highly dynamic and complex nature of future 6G environments. Therefore, ML-driven frameworks approaches have emerged as effective solutions for intelligent traffic management in next-generation wireless communication systems. This paper presents a study of machine learning techniques used for intelligent network traffic management in 6G networks. The paper discusses supervised learning, unsupervised learning, reinforcement learning, and deep learning methods for traffic prediction, congestion control, resource allocation, routing optimization, spectrum management, and network slicing. The role of Artificial Intelligence (AI), edge intelligence, and autonomous network management in future 6G communication systems is also examined. Additionally, important challenges such as computational complexity, real-time processing, security, and data privacy are discussed.

Keywords: 6G Networks, machine learning, intelligent traffic management, artificial intelligence, deep learning, wireless communication

[This article belongs to Journal of Communication Engineering & Systems ]

How to cite this article: Jyoti Amitkumar Dhamecha, Bhavisha Vishalbhai Parvadiya. Machine Learning Approaches for Intelligent Network Traffic Management in 6G. Journal of Communication Engineering & Systems. 2026; 16(02):19-32.
How to cite this URL: Jyoti Amitkumar Dhamecha, Bhavisha Vishalbhai Parvadiya. Machine Learning Approaches for Intelligent Network Traffic Management in 6G. Journal of Communication Engineering & Systems. 2026; 16(02):19-32. Available from: https://journals.stmjournals.com/joces/article=2026/view=259087

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Regular Issue Subscription Review Article
Volume 16
Issue 02
Received 17/05/2026
Accepted 24/06/2026
Published 30/06/2026
Publication Time 44 Days

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