In the realm of network management, the integration of Machine Learning (ML) into routing protocols has emerged as a pivotal strategy for enhancing network efficiency and adaptability. This trend is fueled by the increasing availability of Network Processing Units (NPUs) and the advancement of ML algorithms, enabling real-time analysis of network metrics such as packet loss, latency, and throughput. Embedded within the domain of advanced algorithms, our project ventures into the integration of intelligent systems into network infrastructure, signaling a paradigm shift in network management methodologies. By leveraging ML, network administrators gain valuable insights into optimizing routing decisions, improving network performance, and adapting to dynamic network conditions. Additionally, the integration of ML in routing protocols lays the foundation for future advancements in network automation and optimization. While emphasizing the transformative potential of ML in network management, our project underscores the importance of proactive security measures to safeguard networks against emerging threats.