This work presents a comprehensive analytical review of AI-powered smart city issue detection and management systems, focusing on the integration of artificial intelligence into modern urban governance. With rapid urbanization increasing pressure on infrastructure, transportation, environment, and public services, traditional reactive systems are no longer sufficient. This study highlights how AI technologies such as machine learning, deep learning, computer vision, and natural language processing enable automated detection, classification, and management of urban issues. The paper analyzes smart city architectures, including perception, network, and application layers, along with the role of edge computing for real-time processing. It further explores AI applications across key domains such as traffic management, infrastructure monitoring, environmental quality control, public safety, waste management, energy optimization, and citizen feedback analysis. Additionally, the study evaluates performance metrics, real-world deployment outcomes, and identifies critical research gaps including algorithmic bias, privacy concerns, and lack of explainability. A unified AI-Smart City Integration Framework is proposed to address these challenges and guide future research. This work serves as a reference for researchers, policymakers, and urban planners aiming to develop intelligent, efficient, and sustainable smart city systems.
Aayush Pandey (Tue,) studied this question.
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