This paper explores the integration of Software-Defined Networking (SDN) with Machine Learning (ML) to enhance network performance and Quality of Service (QoS). The study focuses on traffic classification (TC) in SDN environments, comparing traditional methods with ML algorithms. Traditional TC methods, such as port-based, deep packet inspection (DPI), and statistical-based techniques, are discussed for their limitations, while ML algorithms, including supervised learning (SL), unsupervised learning (USL), semi-supervised learning (SSL), and reinforcement learning (RL), are evaluated for their effectiveness in TC. The paper highlights the advantages of ML in SDN, such as improved accuracy, real-time decision-making, and dynamic resource allocation. It also examines how ML can enhance security in SDN by detecting anomalies, intrusions, and attacks, and discusses the challenges and future research directions in integrating ML with SDN for QoS and security. The findings underscore the significant impact of ML techniques in SDN, particularly in traffic classification and security, and suggest areas for further development to address specific problems and improve real-world network performance.This paper explores the integration of Software-Defined Networking (SDN) with Machine Learning (ML) to enhance network performance and Quality of Service (QoS). The study focuses on traffic classification (TC) in SDN environments, comparing traditional methods with ML algorithms. Traditional TC methods, such as port-based, deep packet inspection (DPI), and statistical-based techniques, are discussed for their limitations, while ML algorithms, including supervised learning (SL), unsupervised learning (USL), semi-supervised learning (SSL), and reinforcement learning (RL), are evaluated for their effectiveness in TC. The paper highlights the advantages of ML in SDN, such as improved accuracy, real-time decision-making, and dynamic resource allocation. It also examines how ML can enhance security in SDN by detecting anomalies, intrusions, and attacks, and discusses the challenges and future research directions in integrating ML with SDN for QoS and security. The findings underscore the significant impact of ML techniques in SDN, particularly in traffic classification and security, and suggest areas for further development to address specific problems and improve real-world network performance.