International Journal of Scientific Research and Engineering Development

This paper provides a thorough comparison of traditional Quality of Service (QoS) methods and new AI-based architectures. It looks closely at the main principles, operational models, and performance features of Integrated Services (IntServ) and Differentiated Services (DiffServ). The paper points out the trade-off between reliable guarantees and network scalability. It also introduces the change brought by artificial intelligence and discusses key techniques like Deep Reinforcement Learning (DRL) and predictive analytics, which allow for proactive, dynamic, and automated network management. The discussion highlights the important role of enabling technologies such as Software-Defined Networking (SDN) and 5G in implementing AI-driven QoS. A detailed operational and architectural comparison follows, along with a deep dive into the major challenges and a plan for a future where AI smartly organizes and improves traditional QoS policies. The analysis concludes that while traditional methods offer a solid base, AI is crucial for meeting the needs of dynamic, complex, and intent-driven networks, providing unmatched efficiency, reliability, and flexibility.