<![CDATA[The rapid growth of urban populations and infrastructure complexity has created significant challenges in maintaining the safety, reliability, and efficiency of urban systems. This study proposes an IoT-driven smart infrastructure monitoring and predictive maintenance framework integrated with artificial intelligence (AI) and real-time analytics. The research analyzes the contribution of different monitoring components, including structural sensors (30%), traffic sensors (22%), environmental sensors (18%), energy monitoring devices (16%), and vibration/acoustic sensors (14%). The implementation of intelligent systems demonstrates notable improvements in predictive maintenance accuracy (35%), fault detection (33%), response time (31%), cost efficiency (28%), and operational reliability (27%). The proposed system utilizes a multi-layered architecture comprising sensing, communication, processing, and application layers. Machine learning algorithms are applied to analyze infrastructure data, detect anomalies, and predict potential failures. Edge and cloud computing technologies enhance system performance by enabling real-time processing and scalable data management. The findings highlight the effectiveness of IoT and AI integration in improving infrastructure monitoring and maintenance. The proposed framework supports proactive decision-making, reduces operational risks, and enhances urban sustainability. This research contributes to the development of smart city infrastructures and demonstrates the potential of intelligent systems in modern urban management.]]>
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