As network services become more complex, efficient routing has become crucial for ensuring end-user satisfaction. To address this challenge, researchers are increasingly turning to routing algorithms that integrate Graph Neural Networks (GNNs) with Deep Reinforcement Learning (DRL), leveraging the natural graph structure of network topologies. However, a significant challenge with existing algorithms is their inability to generalize across different topologies without requiring retraining, a constraint that is impractical in real-world applications. To overcome this limitation, we propose a novel GNN-DRL-based routing algorithm, Link2Link, designed to decouple DRL-learned knowledge from specific network topologies by focusing on link-level features. Extensive experiments demonstrate that Link2Link achieves robust performance across diverse topologies, consistently outperforming OSPF without requiring retraining, making it a scalable and adaptable solution for modern network routing challenges.