<p>Interdependent infrastructure systems serve as the foundation of modern society, ensuring the delivery of essential services, economic stability, and public well-being. The increasing integration of these systems, driven by advancements in cyber-physical systems (CPS) and the Internet of Things (IoT), has improved operational efficiency and adaptability. However, this interconnectivity and interdependency have also introduced vulnerabilities, making infrastructure networks more susceptible to cascading failures, cyberattacks, and disruptions from natural disasters and human-induced events. Graph neural networks (GNNs) have emerged as a powerful tool for addressing these challenges, offering data-driven solutions for reliability and resilience analysis across interconnected infrastructure networks. This paper provides a comprehensive review of GNN applications in interdependent infrastructure systems, including transportation, power distribution, water distribution, and communication networks. Reported studies show that GNN-based approaches can reduce prediction error rates by up to 25–30%, improve resilience indices by 15–20%, and shorten recovery times after disruption events by nearly 20% compared with conventional machine learning baselines. By leveraging spatial–temporal modeling, multimodal data integration, and physics-informed learning, GNN-based methods enhance predictive accuracy, system resilience, and decision-making efficiency. This review underscores the potential of GNNs in infrastructure management optimization and risk mitigation, enabling the development of more sustainable, adaptive, and resilient infrastructure systems in the face of evolving challenges.</p> Graphical abstract <p></p>