In light of the increasing complexity and sophistication of contemporary cyber threats, conventional intrusion detection systems (IDS) have proven insufficient in their ability to detect and thwart modern cyber attacks. Machine learning (ML) approaches have emerged as a promising method to augment IDS capabilities by capitalizing on their capacity to identify patterns and anomalies within network traffic. This article presents a thorough investigation into the enhancement of intrusion detection systems through the application of machine learning algorithms. We delve into various ML techniques, their utilization within IDS, and the difficulties associated with implementing ML-based intrusion detection. Furthermore, we propose an innovative framework that amalgamates multiple ML algorithms to enhance the precision and efficiency of intrusion detection.