Network traffic classification is crucial for internet service providers (ISPs) to optimize network performance by identifying various types of applications. Traditional techniques such as Port-Based and Payload-Based are available, but Machine Learning (ML) techniques are the most effective. This research presents a real-time internet data set and utilizes feature extraction tools to extract features from captured traffic, then applies four machine learning classifiers: Support Vector Machine, C4.5 decision tree, Naive Bayes, and Bayes Net classifiers. Results show that the C4.5 classifier achieves the highest accuracy among the other classifiers.