Network security is essential in the linked world of today because critical information systems are the target of an increasing number of malicious attacks and cyberthreats. Intrusion detection systems, or IDS, are primarily responsible for protecting networks against these types of attacks. Through the examination of network traffic characteristics, machine learning algorithms have developed into useful instruments for enhancing intrusion detection systems’ capabilities. In this paper, we provide a comparison of several machine learning techniques for network intrusion detection. We investigate Decision Trees, K-Nearest Neighbors, AdaBoost, Gaussian Naive Bayes, Random Forest, Logistic Regression, and Gradient Boosting in identifying and categorizing network intrusions. For this study, we employ a publicly available network traffic dataset. We evaluate the effectiveness of each technique through multiple experiments, measuring its computational efficiency, accuracy, precision, recall, and F1 score. Our study illustrates the benefits and drawbacks of these algorithms as well as their suitability for use in different intrusion detection scenarios. This study also highlights the significance of selecting appropriate machine learning algorithms that are tailored to the properties of network traffic data to increase the resilience of network security infrastructures.