Rapid urbanization and population growth have intensified challenges in sustainable management of waste and water resources within metropolitan areas. Conventional systems for waste collection, recycling, and water distribution often suffer from inefficiencies, limited adaptability, and environmental impacts, necessitating innovative solutions. Artificial Intelligence (AI) offers transformative potential by enabling predictive analytics, real-time monitoring, and autonomous decision-making in urban resource management. AI-based waste management systems optimize collection routes, automate segregation, and enhance recycling efficiency, supporting circular economy principles and reducing landfill dependency. In parallel, AI-driven water management facilitates leakage detection, demand forecasting, treatment optimization, and wastewater reuse, improving operational efficiency and promoting resilient urban water infrastructures. Integration of AI technologies across waste and water management enables holistic frameworks that enhance resource efficiency, environmental sustainability, and economic feasibility, while aligning with the goals of eco cities. This chapter reviews current advancements, identifies critical research gaps, and presents case studies of cities implementing integrated AI solutions, emphasizing scalability, societal acceptance, and policy alignment. Future directions focus on autonomous, adaptive, and fully sustainable urban systems capable of responding to dynamic environmental and operational challenges. The insights provided contribute to the development of intelligent, data-driven strategies for sustainable urban planning, fostering resilient, efficient, and eco-friendly cities.
Urbanization and industrial expansion have created unprecedented pressures on municipal infrastructure, particularly in the management of waste and water resources [1]. Population growth in metropolitan regions generates large volumes of municipal solid waste, while water demand continues to rise, often exceeding the capacity of existing distribution networks [2]. Conventional waste collection and water management systems, predominantly manual or centralized, struggle to handle such dynamic challenges, leading to operational inefficiencies, environmental degradation, and public health concerns [3]. Ineffective disposal practices contribute to increased greenhouse gas emissions, soil and water contamination, and the depletion of natural resources [4]. Urban authorities are compelled to seek advanced technological solutions that optimize resource utilization while minimizing ecological footprints. Sustainable urban management frameworks increasingly emphasize integration of innovative tools capable of predictive planning, real-time monitoring, and adaptive decision-making to address the multifaceted challenges of modern cities [5].
Eco cities represent a paradigm shift in urban planning, prioritizing environmental sustainability, resource efficiency, and improved quality of life for residents [6]. These cities integrate intelligent systems to monitor, analyze, and optimize waste and water management processes. Artificial Intelligence (AI) plays a critical role in enabling such smart urban ecosystems, offering data-driven solutions to enhance operational efficiency and reduce environmental impacts [7]. By processing vast datasets generated from sensors, IoT devices, and urban networks, AI algorithms identify patterns, predict resource demands, and suggest optimization strategies [8]. Predictive modeling allows municipalities to allocate resources dynamically, anticipate seasonal variations, and implement preventive measures that maintain system reliability [9]. Through intelligent interventions, AI supports the transition from reactive to proactive management practices, ensuring sustainable operation of waste collection, recycling processes, water distribution, and wastewater treatment within urban infrastructures [10].
In waste management, AI facilitates the automation and optimization of critical processes, including collection scheduling, route optimization, and waste segregation [11]. Machine learning algorithms predict waste generation patterns based on demographic, economic, and temporal factors, enabling cities to plan collection operations efficiently [12]. Computer vision systems automate the identification and classification of recyclable materials, improving recovery rates and reducing contamination in recycling streams [13]. Integration of AI with smart bins and IoT sensors allows real-time monitoring of fill levels, collection requirements, and operational bottlenecks. These capabilities enhance resource recovery, lower operational costs, and contribute to circular economy initiatives by promoting material reuse and minimizing landfill reliance [14]. Data-driven insights guide municipalities in policy formulation, operational planning, and strategic investment in recycling infrastructure [15].
