As cybersecurity threats continue to evolve, traditional intrusion detection methods often struggle to keep up with the complexity and volume of modern network traffic. This research addresses these challenges by integrating machine learning techniques into intrusion detection systems (IDS) to enhance their effectiveness in identifying malicious activity. The study explores the use of machine learning algorithms to analyze network traffic in real time, with the aim of improving detection accuracy while reducing false positives. A key component of this research is the integration of dynamic data visualization tools, such as Kibana, which provide intuitive, real-time insights into security events. This visualization enables security professionals to monitor detected threats, understand their context, and make informed decisions quickly. The approach also includes an experimental evaluation of the system’s performance, demonstrating its ability to accurately detect various types of malicious traffic and its potential for scalability in large network environments. The findings of this study contribute to the ongoing evolution of IDS technologies by combining machine learning with real-time traffic analysis and interactive visualization. This research offers a promising approach to proactive cybersecurity defense, providing a more adaptive, efficient, and user-friendly solution for threat detection and mitigation in dynamic network environments