This paper presents a comprehensive study on integrating machine learning (ML) with software-defined networking (SDN) to enhance network performance and quality of service (QoS). The study explores ML classification methods for traffic classification (TC) in SDN, highlighting their significance compared to traditional methods. It discusses how labeled traffic data can be used to train ML models for accurate TC. The research also examines the pros and cons of dynamic and adaptive TC using ML algorithms, as well as how ML can improve SDN security through anomaly detection, intrusion detection, and attack mitigation. The paper identifies challenges and research gaps in SDN-ML integration and emphasizes the need for further investigation into scalability and performance issues in large-scale SDN implementations. The study compares traditional TC methods with ML-based approaches, analyzing their strengths and limitations. Traditional methods include port-based, payload-based, and statistical-based TC, which rely on predefined rules and signatures. ML-based TC, on the other hand, uses algorithms such as supervised learning (SL), unsupervised learning (USL), semi-supervised learning (SSL), and reinforcement learning (RL) to classify traffic based on patterns and features. The paper evaluates various ML algorithms, including decision trees (DT), random forests (RF), support vector machines (SVM), K-nearest neighbors (KNN), and Naïve Bayes (NB), and discusses their performance in TC tasks. The study also explores the application of ML in SDN for QoS, security, and traffic classification. It highlights the benefits of ML in improving network resource management, detecting security threats, and ensuring QoS. The paper presents case studies and experimental results demonstrating the effectiveness of ML in TC, including high accuracy rates achieved by algorithms such as SVM, RF, and hybrid models. The research underscores the potential of ML in enhancing SDN capabilities, while also identifying challenges and areas for further research. Overall, the study concludes that ML has significant potential to improve various aspects of SDN, including security, resource management, and QoS, and emphasizes the need for continued research and development in this area.This paper presents a comprehensive study on integrating machine learning (ML) with software-defined networking (SDN) to enhance network performance and quality of service (QoS). The study explores ML classification methods for traffic classification (TC) in SDN, highlighting their significance compared to traditional methods. It discusses how labeled traffic data can be used to train ML models for accurate TC. The research also examines the pros and cons of dynamic and adaptive TC using ML algorithms, as well as how ML can improve SDN security through anomaly detection, intrusion detection, and attack mitigation. The paper identifies challenges and research gaps in SDN-ML integration and emphasizes the need for further investigation into scalability and performance issues in large-scale SDN implementations. The study compares traditional TC methods with ML-based approaches, analyzing their strengths and limitations. Traditional methods include port-based, payload-based, and statistical-based TC, which rely on predefined rules and signatures. ML-based TC, on the other hand, uses algorithms such as supervised learning (SL), unsupervised learning (USL), semi-supervised learning (SSL), and reinforcement learning (RL) to classify traffic based on patterns and features. The paper evaluates various ML algorithms, including decision trees (DT), random forests (RF), support vector machines (SVM), K-nearest neighbors (KNN), and Naïve Bayes (NB), and discusses their performance in TC tasks. The study also explores the application of ML in SDN for QoS, security, and traffic classification. It highlights the benefits of ML in improving network resource management, detecting security threats, and ensuring QoS. The paper presents case studies and experimental results demonstrating the effectiveness of ML in TC, including high accuracy rates achieved by algorithms such as SVM, RF, and hybrid models. The research underscores the potential of ML in enhancing SDN capabilities, while also identifying challenges and areas for further research. Overall, the study concludes that ML has significant potential to improve various aspects of SDN, including security, resource management, and QoS, and emphasizes the need for continued research and development in this area.