The cyber security business has greatly benefited from the use of artificial intelligence (AI) methods, which offer the potential for highly self-disciplined models capable of detecting and preventing assaults. The Intrusion Detection System (IDS) is a network security solution that was initially developed to identify and detect instances of vulnerability exploits targeting a specific application or computer system. In order to mitigate cyber attacks, the development of Intrusion Detection Systems (IDS) has been undertaken. Currently, our research employs the support vector machine (SVM), Random Forest, and artificial neural network (ANN) techniques. Based on the findings, the accuracy of Support Vector Machines (SVM) was determined to be 97.800005%, while Random Forest achieved an accuracy of 97.816655%. Additionally, Artificial Neural Networks (ANN) demonstrated an accuracy of 94.22222%. Empirical investigation demonstrates that algorithms depending on machine learning exhibit elevated accurateness in detecting assaults compared to conventional approaches, hence enhancing the efficacy of cyber security measures. Furthermore, the use of machine learning algorithms can streamline the process, foster a proactive approach, reduce costs, and significantly bolster effectiveness.