This paper presents a survey on the application of machine learning (ML) in analyzing Internet of Things (IoT) data, focusing on smart city applications. The rapid growth of IoT devices, expected to reach 25-50 billion by 2020, has led to an explosion in data volume, velocity, and variety, creating challenges for data processing and analysis. Smart cities, as a key application area of IoT, generate large volumes of data with characteristics such as high velocity, variety, and dynamic nature. The paper discusses the challenges of processing IoT data and presents a taxonomy of ML algorithms suitable for IoT data analysis. It also explores the use of ML techniques in smart city applications, including traffic prediction using Support Vector Machine (SVM) on Aarhus Smart City data. The paper reviews existing research on IoT data analysis, categorizing algorithms into eight major groups based on their structural similarities, data handling capabilities, and processing efficiency. It highlights the importance of data preprocessing, including data integration, semantic annotation, and quality improvement. The paper also discusses the role of smart data in transforming raw IoT data into actionable insights, emphasizing the need for efficient algorithms that can handle the unique characteristics of IoT data. The study concludes that ML algorithms, particularly those based on supervised and unsupervised learning, are essential for processing and analyzing IoT data in smart cities. The paper provides a comprehensive overview of ML algorithms used in IoT data analysis, including classification, regression, and clustering techniques, and discusses their applications in smart city scenarios. The study emphasizes the importance of selecting appropriate ML algorithms based on data characteristics and application requirements, and highlights the potential of ML in improving the efficiency and effectiveness of IoT applications.This paper presents a survey on the application of machine learning (ML) in analyzing Internet of Things (IoT) data, focusing on smart city applications. The rapid growth of IoT devices, expected to reach 25-50 billion by 2020, has led to an explosion in data volume, velocity, and variety, creating challenges for data processing and analysis. Smart cities, as a key application area of IoT, generate large volumes of data with characteristics such as high velocity, variety, and dynamic nature. The paper discusses the challenges of processing IoT data and presents a taxonomy of ML algorithms suitable for IoT data analysis. It also explores the use of ML techniques in smart city applications, including traffic prediction using Support Vector Machine (SVM) on Aarhus Smart City data. The paper reviews existing research on IoT data analysis, categorizing algorithms into eight major groups based on their structural similarities, data handling capabilities, and processing efficiency. It highlights the importance of data preprocessing, including data integration, semantic annotation, and quality improvement. The paper also discusses the role of smart data in transforming raw IoT data into actionable insights, emphasizing the need for efficient algorithms that can handle the unique characteristics of IoT data. The study concludes that ML algorithms, particularly those based on supervised and unsupervised learning, are essential for processing and analyzing IoT data in smart cities. The paper provides a comprehensive overview of ML algorithms used in IoT data analysis, including classification, regression, and clustering techniques, and discusses their applications in smart city scenarios. The study emphasizes the importance of selecting appropriate ML algorithms based on data characteristics and application requirements, and highlights the potential of ML in improving the efficiency and effectiveness of IoT applications.