Intelligent Intrusion Detection Systems (IDS) are pivotal in safeguarding computer networks against unauthorized access and cyber threats. These systems are engineered to detect, identify, and classify potential attacks, while also recognizing security vulnerabilities, thereby enabling timely alerts for network administrators. This study delves into the application of the Random Forest algorithm as the core technique for intelligent intrusion detection. The efficacy of the proposed approach was evaluated using the NSL-KDD dataset, a widely recognized benchmark in intrusion detection research. This dataset comprises 125,973 samples with 41 distinct features representing various network traffic characteristics. The Random Forest algorithm, known for its ensemble-based nature, constructs multiple decision trees during training and outputs the class that is the mode of the classes (classification) of the individual trees. This method enhances predictive accuracy and controls overfitting. Experimental results indicate that the use of this algorithm significantly improves the accuracy of intrusion detection, achieving a remarkable detection rate of 99.89%. These findings underscore the potential of Random Forest in developing intelligent and reliable IDS, offering a robust solution for real-world network security applications. The study also discusses the algorithm's performance in terms of precision, recall, and F1-score, highlighting its effectiveness in various attack scenarios.