The reliability of urban water distribution networks (WDNs) is critical for ensuring sustainable infrastructure management. However, traditional failure prediction models often overlook the complex interdependencies between water pipelines and road networks, leading to suboptimal predictive accuracy. This study introduces a novel pipeline failure prediction framework that leverages Graph Neural Networks (GNNs) to incorporate coupled road–pipeline network features. By integrating traffic-related indicators, such as intersection proximity, pipeline–road angles, and network topology, this approach systematically assesses their impact on failure risk. A comparative evaluation of various GNN architectures, including Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and GraphSAGE, demonstrates that GraphSAGE achieves the highest predictive performance, significantly surpassing traditional machine learning methods. The findings underscore the necessity of incorporating network topology into predictive models, validating the role of spatial dependencies in accurately assessing pipeline failure risks. This study contributes to advancing infrastructure resilience modeling by providing a robust predictive framework that supports proactive maintenance strategies and enhances risk mitigation in urban water distribution systems.