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 IoT is a vast network of interconnected devices that have transformed daily life by integrating technology into various domains such as smart cities, smart homes, and healthcare. However, the rapid growth of IoT devices has introduced significant security and privacy challenges, including vulnerabilities like node spoofing, unauthorized data access, and cyberattacks such as denial of service (DoS), eavesdropping, and intrusion detection. ML and DL have emerged as robust solutions to address these security issues in IoT devices. The paper reviews recent research on IoT security focusing on ML/DL approaches, categorizes studies based on ML/DL solutions, and highlights their opportunities, advantages, and limitations. It also identifies key security challenges and threats that hinder IoT applications and presents a new taxonomy in the field of artificial intelligence. The paper discusses the IoT architecture, which consists of three layers: the terminal perception layer, the network layer, and the application layer. Each layer has unique security concerns, including data transmission, processing, and storage. The paper examines various security challenges in IoT, such as cyberattacks, eavesdropping, DoS, unauthorized data access, and intrusion detection. It presents a range of security solutions based on ML and DL in IoT environments, emphasizing the effectiveness of these techniques in enhancing security. The paper also discusses the challenges ahead, potential areas for further research, and future perspectives in IoT security. The paper compares different ML and DL methods for attack detection, highlighting their strengths and weaknesses. It discusses the applications of ML and DL in IoT security, including intrusion detection, malware detection, and data analysis. The paper also addresses the importance of privacy in IoT ecosystems and the need for robust security measures such as encryption, access control, and intrusion detection systems. The paper concludes that ML and DL techniques are essential for enhancing the security of IoT systems. These techniques offer versatile and effective solutions for addressing security challenges in IoT environments. The paper emphasizes the need for further research and development in this area to ensure the sustainable growth and secure deployment of IoT systems.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 IoT is a vast network of interconnected devices that have transformed daily life by integrating technology into various domains such as smart cities, smart homes, and healthcare. However, the rapid growth of IoT devices has introduced significant security and privacy challenges, including vulnerabilities like node spoofing, unauthorized data access, and cyberattacks such as denial of service (DoS), eavesdropping, and intrusion detection. ML and DL have emerged as robust solutions to address these security issues in IoT devices. The paper reviews recent research on IoT security focusing on ML/DL approaches, categorizes studies based on ML/DL solutions, and highlights their opportunities, advantages, and limitations. It also identifies key security challenges and threats that hinder IoT applications and presents a new taxonomy in the field of artificial intelligence. The paper discusses the IoT architecture, which consists of three layers: the terminal perception layer, the network layer, and the application layer. Each layer has unique security concerns, including data transmission, processing, and storage. The paper examines various security challenges in IoT, such as cyberattacks, eavesdropping, DoS, unauthorized data access, and intrusion detection. It presents a range of security solutions based on ML and DL in IoT environments, emphasizing the effectiveness of these techniques in enhancing security. The paper also discusses the challenges ahead, potential areas for further research, and future perspectives in IoT security. The paper compares different ML and DL methods for attack detection, highlighting their strengths and weaknesses. It discusses the applications of ML and DL in IoT security, including intrusion detection, malware detection, and data analysis. The paper also addresses the importance of privacy in IoT ecosystems and the need for robust security measures such as encryption, access control, and intrusion detection systems. The paper concludes that ML and DL techniques are essential for enhancing the security of IoT systems. These techniques offer versatile and effective solutions for addressing security challenges in IoT environments. The paper emphasizes the need for further research and development in this area to ensure the sustainable growth and secure deployment of IoT systems.