An AI-Driven Intelligent Network Optimization Framework Using Machine Learning and Deep Learning Techniques for Efficient Traffic Routing, Dynamic Resource Allocation, and Quality of Service Enhancement in Modern Computer Networks

Abstract

The rapid growth of modern computer networks, driven by data-intensive applications, Internet of Things ecosystems, and emerging next-generation communication technologies, has introduced significant challenges in efficient traffic management, dynamic resource allocation, and Quality of Service assurance. Traditional network optimization techniques often rely on static configurations and rule-based mechanisms, which are inadequate for handling the complexity and dynamic nature of contemporary network environments. To address these limitations, this paper proposes an AI-driven intelligent network optimization framework that leverages machine learning and deep learning techniques to enable adaptive, data-driven decision-making. The proposed framework integrates multiple AI paradigms, including supervised learning for traffic prediction, deep neural networks for pattern recognition, and reinforcement learning for real-time routing and resource allocation decisions. By combining these approaches, the system is capable of dynamically adapting to varying network conditions, such as traffic congestion, fluctuating bandwidth demands, and node failures. A multi-objective optimization strategy is employed to simultaneously enhance key performance indicators, including throughput, latency, packet delivery ratio, and overall QoS. Furthermore, the framework incorporates a feedback-driven learning mechanism that continuously refines its decision policies based on real-time network performance metrics. This enables the system to achieve higher levels of scalability, robustness, and efficiency compared to conventional methods. The proposed architecture can be seamlessly integrated with Software-Defined Networking environments, allowing centralized control and improved network programmability. Extensive simulations are conducted using standard network simulation tools to evaluate the performance of the proposed model against traditional routing protocols and baseline machine learning approaches. The results demonstrate significant improvements in network performance, including reduced end-to-end delay, increased throughput, and enhanced QoS stability under dynamic traffic conditions.