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In this paper, we introduce a scalable and modular combination of edge–cloud computing, IoT, and machine learning (ML)-powered architecture for intelligent cities. The framework tackles fundamental urban issues such as energy management, pollution control, and traffic management with the help of smart decision-making and real-time data analysis. Its multi-layer architecture provides seamless interaction of data among cloud machine learning models and IoT sensors. The efficiency of the system is demonstrated with the use of traffic flow prediction as an example. Results of the performance evaluation affirm increased operational efficiency, scalability, and responsiveness. Architectural suggestions create the foundation for intelligent, agile, and eco-friendly cities.

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Machine Learning-Driven Smart City Architecture: A Scalable Design Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Rajdeep Das,
  • Anand Ghugare

摘要

In this paper, we introduce a scalable and modular combination of edge–cloud computing, IoT, and machine learning (ML)-powered architecture for intelligent cities. The framework tackles fundamental urban issues such as energy management, pollution control, and traffic management with the help of smart decision-making and real-time data analysis. Its multi-layer architecture provides seamless interaction of data among cloud machine learning models and IoT sensors. The efficiency of the system is demonstrated with the use of traffic flow prediction as an example. Results of the performance evaluation affirm increased operational efficiency, scalability, and responsiveness. Architectural suggestions create the foundation for intelligent, agile, and eco-friendly cities.