AI-Enhanced Smart Infrastructure Compliance Systems for Predictive Maintenance in Urban Energy Management Projects

As cities transition toward intelligent, sustainable infrastructures, the integration of artificial intelligence (AI) into compliance systems becomes essential for managing complex urban energy environments. Traditional infrastructure compliance models are reactive, data-siloed, and inefficient, leading to unplanned maintenance, safety violations, and energy losses. This review explores how AI-enhanced compliance systems can enable predictive maintenance, ensure regulatory alignment, and optimize operational efficiency in smart infrastructure projects. The paper investigates AI applications in real-time monitoring, anomaly detection, and failure prediction across urban energy assets such as smart grids, HVAC systems, and renewable energy platforms. It also examines the use of machine learning models for compliance verification, risk scoring, and automated reporting in dynamic urban settings. Emphasis is placed on integrating AI with Internet of Things (IoT) networks and Building Information Modeling (BIM) for seamless data ingestion, contextual analysis, and lifecycle asset management. Case studies from global urban energy projects illustrate practical applications, performance benefits, and deployment challenges. Finally, the review outlines key barriers, policy recommendations, and future research directions for scalable, trustworthy, and intelligent infrastructure compliance solutions that support sustainable urban growth and energy resilience.