The rapid growth of interconnected digital gadgets and technological advancements has turned data security into a significant issue because it has augmented cyberattacks. An intrusion detection system (IDS) is necessary for a system or network to efficiently identify and examine harmful traffic. A hardware or software-based technology called an intrusion detection system (IDS) is used to monitor network traffic and identify unusual activity. With increased computer interaction, intrusion detection is becoming more and more crucial to network security. IDS systems closely monitor network traffic in order to identify and alert users about possible threats or maliciousness. This study aims to establish a smart intrusion detection system; this solution depends on various machine learning techniques. The effectiveness of the system is measured in different metrics that are compared to other solutions in determining the best intrusion detection method. Through the evaluation of the results of different studies, valuable insights are obtained to be applied in the development of this area in the future. Data breaches are extremely harmful for privacy and confidentiality concerns since typically entail the unauthorized access, alteration, or destruction of sensitive data. These may cause service interruptions, such as downtimes, and cause disastrous legal, financial and reputational losses of organizations.