The Malicious network activities such as malware attacks, unauthorized intrusions, and data theft have become a rising threat as organizations increasingly rely on interconnected systems. Detecting these security incidents rapidly and accurately is critical, but also poses challenges due to the constantly evolving techniques used by hackers. Machine learning has surfaced as a promising avenue for developing intelligent systems capable of analyzing massive amounts of network data, identifying anomalies, and detecting malicious patterns in real-time. This research presents a thorough comparative evaluation of the key machine learning algorithms applied for detecting various types of malicious network traffic. The models tested include random forests, regular neural networks, and logistic regression. Using labeled datasets containing benign and attack traffic flows, the algorithms are trained, and their performance evaluated across parameters like detection accuracy, confusion matrix, F1-scores, and receiver operating characteristics. The results demonstrate that ensemble methods such as random forests achieve exceptionally high accuracy, exceeding 99% in some tests, by combining diverse decision trees. With careful tuning of hyperparameters, neural networks also display comparable performance owing to their automated feature extraction capabilities. The analysis provides useful insights into the strengths, weaknesses, and applicability of each technique. These findings will enable security experts to select the most appropriate machine learning solutions tailored to their network environments and requirements for developing robust intrusion detection systems capable of identifying threats in real time.