This paper provides a comprehensive survey of the use of machine learning (ML) and deep learning (DL) methods in securing the Internet of Things (IoT). The authors review recent research on IoT security, focusing on the challenges and vulnerabilities that arise with the rapid growth of IoT devices and applications. They categorize security issues based on ML/DL solutions, highlighting their opportunities, advantages, and limitations. The paper discusses the architecture of IoT, including the application, network, and perception layers, and explores various security threats such as node spoofing, unauthorized data access, and cyberattacks like DoS, eavesdropping, and intrusion detection. The authors also analyze state-of-the-art IoT-specific challenges and present a new taxonomy in the field of artificial intelligence. The main contributions of the paper include a comprehensive discussion of IoT security challenges, an examination of inherent vulnerabilities and cyber threats, and a detailed analysis of ML/DL methods for IoT security. The paper concludes by expressing various prospective research challenges and future pathways for the application of ML/DL to ensure the security of IoT.This paper provides a comprehensive survey of the use of machine learning (ML) and deep learning (DL) methods in securing the Internet of Things (IoT). The authors review recent research on IoT security, focusing on the challenges and vulnerabilities that arise with the rapid growth of IoT devices and applications. They categorize security issues based on ML/DL solutions, highlighting their opportunities, advantages, and limitations. The paper discusses the architecture of IoT, including the application, network, and perception layers, and explores various security threats such as node spoofing, unauthorized data access, and cyberattacks like DoS, eavesdropping, and intrusion detection. The authors also analyze state-of-the-art IoT-specific challenges and present a new taxonomy in the field of artificial intelligence. The main contributions of the paper include a comprehensive discussion of IoT security challenges, an examination of inherent vulnerabilities and cyber threats, and a detailed analysis of ML/DL methods for IoT security. The paper concludes by expressing various prospective research challenges and future pathways for the application of ML/DL to ensure the security of IoT.