The increasing volume and complexity of network traffic pose significant challenges to traditional network security systems. Machine learning (ML) and deep learning (DL) have emerged as promising solutions to secure traffic in computer networks. This paper provides an overview of the role of machine learning and deep learning in network security, including different types of machine and deep learning techniques and their applications in intrusion detection and prevention, malware detection, network anomaly detection, and other areas. The paper also discusses the challenges and limitations of using machine and deep learning for network security and provides case studies of real-world implementations. Finally, potential future directions for research in this area are explored. The findings of this paper highlight the potential of machine learning for securing traffic in computer networks and the need for continued research and development in this area.