For a network to function properly and remain secure, network traffic management and analysis are essential. In this field, machine learnin g-based techniques have demonstrated considerable potential by offering precise and effective network traffic analysis and anomaly detection. In this research, we offer a machine learning-based methodology for network traffic monitoring and management. Thi s method analyses network data and identifies network anomalies using a variety of machine learning methods. Using the NSL -KDD dataset and other machine learning methods, such as decision trees, SVM, neural networks, and random forests, we assess the effec tiveness of our strategy. The outcomes of our tests show how successful our suggested strategy is, with high accuracy rates and low false positive rates. In numerous network management and security applications, our suggested approach beats cutting -edge machine learning-based algorithms for network traffic analysis and management. The suggested strategy offers a positive perspective for improving network administration and security through machine learning.