In the dynamic field of 5G network monitoring, the ability to generalize monitors' placement across a network is crucial for comprehensive coverage. This study introduces the use of the Relational Graph Convolutional Network (RGCN) model to meet this challenge. We conducted a comparative analysis between the RGCN and a traditional Neural Network (NN) across two network topologies, considering every possible node configuration within these topologies. Our findings indicate that the RGCN model, once trained on a specific node pair, exhibits superior generalization ability and accuracy. It consistently transfers its learning to changes in monitors' placement and accurately estimates links delays beyond its initial training monitors. Unlike the NN, which showed significant limitations in generalizing monitors' placement and high error rates. This paper not only demonstrates the effectiveness of the RGCN model in generalizing the monitors' placement problem but also paves the way for its broader application in dynamic network monitoring contexts.