Artificial Intelligence in Network Optimization: Addressing Latency, Congestion, and Bandwidth Challenges through Predictive Analytics and Automation

The exponential growth of network traffic driven by cloud computing, IoT, 5G, and data-intensive applications has placed immense pressure on traditional network infrastructures. These systems often struggle with latency, congestion, bandwidth limitations, and the complexity of real-time management. Conventional rule-based network optimization methods are reactive and lack scalability, making them insufficient for the dynamic requirements of modern digital services. This research explores the transformative role of Artificial Intelligence (AI) in optimizing network performance through predictive analytics, automation, and self-optimization. By analyzing current challenges and AI-based solutions, the study highlights how machine learning algorithms and real-time analytics can proactively manage network traffic, detect anomalies, enhance Quality of Service (QoS), and reduce operational costs. The paper also investigates the practical implementation of AI in real-world environments, identifying barriers to adoption and best practices for successful deployment. The findings demonstrate that AI not only improves network efficiency and adaptability but also provides a scalable framework for intelligent, secure, and self-healing network infrastructures.