Urbanization and rapid population growth have intensified the demand for efficient, resilient, and sustainable urban infrastructure and energy systems. Traditional approaches to urban management are often insufficient to address the complexity, dynamic behavior, and uncertainty inherent in modern cities. Machine learning (ML) has emerged as a transformative technology that enables data-driven decision-making, predictive analytics, and optimization across diverse urban domains. This chapter explores the application of ML techniques in enhancing energy management, urban infrastructure operations, and smart city development. Key areas of focus include predictive maintenance of infrastructure assets, optimization of microgrids and distributed energy resources, traffic and water system monitoring, and integration with cyber-physical systems and digital twins. The chapter also examines hybrid ML frameworks that combine multiple algorithms to improve model accuracy, adaptability, and operational efficiency. Challenges related to data heterogeneity, privacy, scalability, and model interpretability are critically analyzed, highlighting opportunities for future research. By leveraging ML-driven frameworks, cities can achieve sustainable energy utilization, enhanced operational resilience, and improved quality of urban life, supporting long-term environmental and societal objectives.
Rapid urbanization and population growth have created unprecedented challenges for modern cities, significantly impacting infrastructure, energy demand, and environmental sustainability [1]. Urban systems now encompass highly interconnected networks, including transportation, water supply, energy grids, and waste management, each generating vast volumes of heterogeneous data [2]. Conventional management approaches, often based on static models and historical trends, fail to address the dynamic, nonlinear, and stochastic nature of these systems [3]. The growing complexity requires innovative, data-driven methodologies capable of optimizing resource allocation, minimizing operational inefficiencies, and supporting sustainable development. Machine learning (ML) has emerged as a transformative tool in this context, offering predictive, prescriptive, and adaptive solutions that can process large-scale urban data and provide actionable insights [4]. By enabling intelligent decision-making, ML facilitates the design of resilient urban infrastructure, supports energy efficiency, and reduces the environmental footprint of urban systems, contributing to the broader goals of sustainable urban development [5].
Energy management represents one of the most critical challenges in urban infrastructure [6]. The increasing integration of renewable energy sources, such as solar and wind, introduces variability in supply, creating a need for adaptive forecasting and real-time optimization of generation and consumption [7]. Machine learning algorithms, including supervised learning, unsupervised clustering, and reinforcement learning models, provide the capability to forecast demand patterns, detect anomalies, and optimize energy distribution across microgrids and distributed energy resources [8]. These approaches improve operational efficiency, reduce reliance on fossil fuels, and enhance the integration of renewable energy into urban grids [9]. Predictive models can assist in demand-side management, enabling intelligent consumption patterns that respond to dynamic pricing, system constraints, or environmental targets. The resulting optimization not only reduces operational costs but also contributes to minimizing greenhouse gas emissions, fostering energy sustainability in urban areas [10].
Urban infrastructure extends beyond energy systems to include transportation, water networks, and waste management, all of which are affected by population growth, climate variability, and environmental pressures [11]. Transportation networks, for instance, face congestion, delays, and pollution due to inefficient routing and high vehicle density [12]. Machine learning models can analyze historical and real-time traffic data to predict congestion hotspots, optimize signal timings, and suggest alternative routing strategies [13]. Similarly, water distribution systems can benefit from ML-based monitoring to detect leaks, prevent overuse, and enhance resource efficiency. Waste management operations can also be optimized using predictive analytics for collection scheduling and route planning [14]. By leveraging large datasets from IoT sensors and monitoring systems, ML provides a framework to improve the reliability, efficiency, and sustainability of urban infrastructure, ensuring that resources are utilized effectively while minimizing environmental and social impacts [15].
