Intrusion detection systems (IDS) are employed to investigate anomalous behavior in a network system, which monitors a network system for suspicious behavior, which is essential for maintaining network security. Improving accuracy in intrusion detection is necessary to lower false alarms and boost detection rates. Support Vector Machine (SVM—Linear and Quadratic), Long Short-Term Memory (LSTM), and k-nearest neighbors (kNN), machine learning techniques for intrusion detection in network environments, are compared in this research. The effectiveness of SVM, which is well-known for its resilience in high-dimensional environments, in differentiating between normal and malicious behavior is examined. Straightforward yet powerful algorithms, namely KNN and LSTM, are analyzed to see how well they can adjust to different types of intrusions. Regarding detection accuracy, false positive rates, and response times, the experimental results on a benchmark intrusion detection dataset highlight the advantages and disadvantages of the models considered for the study. This study suggests incorporating machine-learning approaches into real-time intrusion detection systems to improve network security and lessen cyber risks.