| Abstract | The rapid urbanization and technological advancements in recent years have paved the way for the
development of smart cities, aiming to enhance the quality of life, sustainability, and efficiency of
urban areas. This survey provides a comprehensive overview of algorithms employed in intelligent
computing for smart city applications. The focus is on algorithms that play a pivotal role in
addressing the multifaceted challenges associated with urban environments, including traffic
management, energy optimization, public safety, and human-centric analytics. Various machine
learning techniques, such as supervised and unsupervised learning, as well as deep learning, are
explored for their applications in tasks like predictive analytics, image analysis, and behaviour
modelling. The survey also delves into the significance of edge computing and fog computing in
optimizing real-time data processing and decision-making at the local level. In conclusion, this
survey offers a comprehensive roadmap of algorithms that form the backbone of intelligent
computing in smart city applications. By synthesizing insights from diverse research domains, it
aims to provide researchers, practitioners, and policymakers with a holistic understanding of the
current state-of-the-art and future directions in this rapidly evolving field.
Keywords: Internet of Things (IoT), Machine learning, Edge Computing, Fog Computing, Smart
City,IntelligentComputing
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