The convergence of artificial intelligence, digital twin technology, and predictive structural health monitoring is fundamentally transforming the management of urban infrastructure systems. This review synthesizes current advances in AI-driven methodologies for civil infrastructure, examining machine learning frameworks for damage detection, digital twin architectures for real-time asset simulation, and integrated predictive maintenance strategies. The objective is to provide a comprehensive conceptual framework for understanding how these technologies collectively enable resilient urban development. Key methodological approaches examined include convolutional neural networks and autoencoders for vibration-based structural health monitoring, long short-term memory models for time-series prediction, and federated learning architectures for privacy-preserving analytics across distributed sensor networks. Digital twin frameworks integrating internet of things data streams, physics-based modeling, and simulation layers are analyzed for their capacity to enable what-if scenario analysis and closed-loop control. Application domains encompass bridge and transportation network monitoring, high-rise structural assessment, and climate-adaptive urban systems. Comparative evaluation reveals that hybrid AI–IoT–digital twin architectures achieve significant improvements in response time and maintenance cost reduction while enhancing predictive accuracy. Critical implementation challenges include data quality and interoperability limitations, high computational demands, workforce training gaps, and cybersecurity vulnerabilities. Governance considerations encompass the need for standardized regulatory frameworks, transparent algorithmic decision-making, and ethical AI protocols. Future research directions include edge computing integration for real-time analytics, physics-informed neural networks combining data and mechanics, and explainable AI methodologies for interpretable damage diagnostics. The review concludes that systematically integrated AI-driven infrastructure systems offer a scalable foundation for predictive, resilient, and sustainable urban development.