AI-Driven Traffic Prediction and Congestion Control in Smart Communication Systems

Abstract

The rapid expansion of smart communication systems, supported by advanced technologies such as fifth-generation (5G), sixth-generation (6G), Internet of Things (IoT), edge computing, cloud networking, and intelligent transportation infrastructures, has significantly increased network traffic complexity and communication demands. Traditional traffic prediction and congestion control mechanisms often struggle to efficiently manage dynamic traffic patterns, high mobility, massive device connectivity, and real-time service requirements in modern communication environments. Artificial Intelligence (AI)-driven traffic prediction and congestion control have emerged as effective solutions for enhancing network performance through intelligent analytics, adaptive learning, and autonomous decision-making capabilities. This study investigates the application of AI techniques, including Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Neural Networks, and predictive analytics, for traffic forecasting and congestion management in smart communication systems. The research explores how AI-enabled models improve traffic prediction accuracy, optimize routing strategies, reduce packet loss, minimize communication delays, and enhance Quality of Service (QoS) across heterogeneous communication infrastructures. Furthermore, the study examines key challenges associated with AI-driven traffic management, including data heterogeneity, computational complexity, scalability, security risks, privacy concerns, and real-time processing limitations. By analyzing recent developments and intelligent networking strategies, the paper highlights the transformative role of AI in improving traffic flow optimization, network resilience, congestion avoidance, and intelligent resource allocation in smart communication ecosystems. The findings indicate that AI-powered traffic prediction and congestion control frameworks significantly enhance communication efficiency, reduce latency, improve bandwidth utilization, and support the development of autonomous and intelligent communication systems for future smart cities and connected environments.