In 2024, Forbes reported around 100 annual incidents affecting undersea cables, disrupting global telecommunications. A recent major cable cut between Asia and Europe severely impacted network quality and customer experience. Network engineers typically explore all routing paths, including IX, Peering, and Transit services, but navigating these diverse resources can lead to further issues if done incorrectly. Once traffic is rerouted optimally, persistent service degradation, especially at the application layer, complicates problem identification. Manually analyzing performance across platforms is labor-intensive. AI offers a solution, with GenAI continuously learning traffic behaviors and optimizing resource allocation. GenAI will help to prioritizes traffic based on Quality of Service (QoS) metrics and preemptively routes traffic to avoid performance issues. Enhanced API integration facilitates proactive communication between platforms, improving network resilience and predictive maintenance capabilities. AI-driven solutions stabilize service quality, enhance customer confidence, and foster loyalty. This innovation represents a crucial step towards automated network management, ensuring robust service delivery amidst evolving telecommunications challenges. Applying AI to cut off the complex manual process of traffic & service engineering, also to prevent any mistake during the manual work. With predictive AI, it can also provide many useful information related to the outage for better decision making. Implement Zero-Touch Orchestration, Operations, and Management best practices to achieve fully automated, self-healing networks that can intelligently prioritize QoS and manage outages without human intervention. In terms of business perspective, network or service degradation could potentially harm the customer and impact their business, it also directly impacts their service and business continuity, manual effort of traffic engineering is not efficient in helping to restore the service, it takes a lot of time and manual effort. In this proposed solution, GenAI could use its predictive AI functionality to analyze the traffic behavior and parameters across application, as well as its QoS, learning the routing & application parameter, latency, path, etc before defining the best route possible to carry the traffic. GenAI also communicates with the application layer to extract some parameters defining the real situation that the customer is facing, and to find out the level of service it needs to be adjusted and compromised.