An Insight Into Smart Infrastructure With Artificial Intelligence-Driven Predictive Maintenance: Transforming the Future of Urban Systems

The advancement of urban infrastructure is undergoing a transformative shift with the integration of artificial intelligence (AI), reshaping traditional maintenance strategies into dynamic, data-driven processes. AI-enabled predictive maintenance stands at the forefront of this evolution, facilitating real-time assessment and preemptive interventions to mitigate system inefficiencies and prevent failures. This paper examines the role of AI in predictive maintenance, showcasing its ability to harness data from IoT sensors, machine learning models, and analytics to optimize the management of infrastructure assets, including transportation networks, bridges, energy systems, and utilities. Key techniques such as anomaly detection, fault diagnosis, and predictive forecasting are explored, alongside the challenges of data integration, model scalability, and system reliability. The study highlights the economic and environmental benefits of AI-enabled predictive maintenance, including reduced operational disruptions, cost savings, prolonged asset lifespan, and enhanced sustainability. Offering a comprehensive review, this work underscores the transformative impact of AI in creating resilient, efficient, and sustainable smart city ecosystems while identifying critical research opportunities for advancing its practical implementation.