(untitled) (International Journal of Engineering & Extended Technologies Research (IJEETR), 2024)
Modern computer networks face unprecedented challenges in maintaining optimal throughput amid dynamic traffic patterns, varying bandwidth demands, and unpredictable network conditions. Traditional static optimization approaches prove inadequate for contemporary networks where conditions change rapidly and unpredictably. This research proposes an AI-driven adaptive optimization framework that continuously monitors network parameters and dynamically adjusts routing, bandwidth allocation, and congestion control mechanisms to maximize communication throughput. The framework employs machine learning algorithms to predict traffic patterns, identify bottlenecks, and implement real-time optimizations that adapt to changing network conditions. Through comprehensive evaluation across diverse network scenarios, we demonstrate that the proposed framework achieves substantial throughput improvements compared to conventional approaches while maintaining stability and fairness. The research contributes both theoretical foundations for adaptive network optimization and practical implementation strategies applicable to enterprise, data center, and telecommunications networks. Our findings indicate that AI-driven adaptive optimization can increase average network throughput by up to forty percent while reducing latency variability and improving overall quality of service.