Sarcouncil Journal of Engineering and Computer Sciences

Abstract: Urban evolution through integrated intelligent technologies represents a paradigm shift in metropolitan resource management and citizen service delivery. Machine learning integrated with advanced network intelligence systems creates dynamic urban ecosystems that enable real-time decision-making and predictive analytics across various domains. Smart cities generate enormous volumes of data through networked sensor networks, IoT devices, and communication infrastructure, requiring sophisticated processing capabilities to extract valuable insights for urban optimization. Computer vision and sensor networks enable real-time traffic management systems to dynamically adjust signal timing, predict congestion patterns, and recommend optimal routes in real-time, resulting in significant improvements in mobility efficiency and fuel savings. Predictive maintenance systems for IoT infrastructure utilize anomaly detection algorithms and failure pattern recognition to identify potential device malfunctions before critical failures occur, enabling proactive maintenance scheduling and resource allocation optimization. Energy management systems integrate machine learning with smart grid technologies to predict demand patterns, manage distributed resources, and optimize generation operations while minimizing non-renewable energy consumption. Network infrastructure reliability is enhanced through intelligent fault detection mechanisms, automated failover systems, and cybersecurity applications that protect critical city services from diverse threats. The convergence of these technologies enables cities to transition from reactive management approaches to proactive data-driven strategies that improve quality of life while maximizing resource efficiency and operational performance.