Intelligent Predictive Models for Smart City Infrastructure Management

Abstract

The development of smart cities relies heavily on intelligent predictiveanalytics to ensure sustainable, efficient, and citizen-centric infrastructuremanagement. This paper examines how predictive models powered by bigdata, Internet of Things (IoT), and machine learning algorithms can optimizeurban services such as energy distribution, traffic control, waste management, and water supply. By harnessing real-time sensor data and historical records, predictive analytics can forecast peak energy demands, traffic congestion trends, and waste collection needs, thereby reducing costs and environmental impacts. The study highlights successful case studies from cities that have adopted predictive data-driven governance and discusses how intelligent systems can improve quality of life while fostering long-term sustainability. In addition, challenges such as data integration, cybersecurity threats, and the digital divide are critically examined to provide a balanced perspective on the smart city transformation. KEYWORDS: Smart cities, Predictive analytics, Intelligent data,Infrastructure management, IoT applications