In today’s digital era, network traffic has become increasingly complex and unpredictable due to the explosive growth of connected devices and data-intensive applications. Efficient real-time network traffic prediction and management are vital to ensure smooth communication, reduce latency, prevent congestion, and optimize resource allocation. Machine learning (ML) models have emerged as a powerful tool to tackle these challenges, offering intelligent, adaptive solutions for dynamic network environments.

Why Real-Time Network Traffic Prediction Matters

Network operators need to anticipate traffic patterns to make informed decisions about routing, bandwidth allocation, and congestion control. Real-time traffic prediction helps in:

• Enhancing Quality of Service (QoS): By forecasting traffic spikes, networks can proactively manage resources to maintain service quality.
• Reducing Congestion: Early predictions allow network systems to reroute data or balance loads to avoid bottlenecks.
• Optimizing Network Utilization: Adaptive bandwidth management based on predictions leads to better use of existing infrastructure.
• Supporting Emerging Technologies: Applications like