Intrusion Detection System (IDS) leveraging advanced machine learning techniques, notably the Random Forest algorithm, to enhance network security against evolving cyber threats. Traditional IDS systems often struggle with new attack types and processing efficiency. By employing Gaussian Naive Bayes, Decision Trees, Logistic Regression, Random Forest, and Gradient Classifier, the proposed system aims to effectively recognize patterns and identify intrusions even in complex and limited data scenarios. Specifically, the Random Forest algorithm is highlighted for its ability to handle high-dimensional and noisy network traffic data, adapt to diverse intrusion scenarios, and exhibit robust classification performance. The system architecture involves data pre-processing, feature engineering, and model development, culminating in real-time implementation. Advantages include robustness to complexity, adaptability to diverse scenarios, efficient noise handling, high performance, precision in detection, minimized false alarms, scalability, and computational efficiency. The system architecture encompasses multi-tiered data collection, pre-processing, detection engines, decision-making modules, centralized management consoles, logging, and reporting mechanisms. Continuous adaptation and integration within the broader security ecosystem ensure swift detection, response, and mitigation of intrusions in network environments. Overall, the proposed IDS system presents a comprehensive approach to bolstering network security against modern cyber threats.