The rapid growth of internet services, cloud computing, Internet of Things (IoT), multimedia streaming, and 5G/6G communication networks has significantly increased network complexity and traffic volume. Modern communication infrastructures must handle dynamic traffic patterns while maintaining high performance, low latency, and efficient resource utilization. Traditional traffic management approaches often struggle to accurately predict network congestion and changing user demands due to the highly dynamic nature of communication environments. Machine Learning (ML) has emerged as a powerful solution for network traffic prediction by enabling intelligent analysis of historical and real-time network data. ML-based traffic prediction techniques facilitate proactive resource allocation, congestion management, quality of service optimization, and network automation. This paper presents a comprehensive study of Machine Learning for Network Traffic Prediction and proposes an Artificial Intelligence-Based Traffic Prediction Framework (AI-TPF) designed to improve network efficiency and reliability. The proposed framework integrates deep learning models, real-time traffic analytics, adaptive resource allocation, and predictive network management mechanisms. Experimental evaluation demonstrates significant improvements in prediction accuracy, throughput optimization, latency reduction, and network utilization compared with conventional traffic forecasting methods. The findings indicate that machine learning-driven traffic prediction will become a fundamental technology for future intelligent communication networks and autonomous network management systems.

Keywords— Network Traffic Prediction, Machine Learning, Artificial Intelligence, Deep Learning, Network Management, 6G Networks, Traffic Forecasting, Communication Systems.