The potential for crimes and attacks poses a potential threat to real-world personal computer networks, presenting risks such as service outages, data breaches, and financial losses. Due to the failure of traditional methods to remain aware of the rapidly changing nature of risks, traditional intrusion detection systems (IDS) typically have high false positive rates and reduce detection accuracy. The use of meta-learning (ML) can advance the development of discontinuity identification frameworks by using their ability to distinguish between examples and ways of eliminating information. This paper explores the application of meta-learning in intrusion detection using the NSL_KDD dataset. By leveraging meta-learning techniques, the study aims to enhance the performance of traditional IDS. Different machine learning algorithms are evaluated and compared. The results demonstrate the effectiveness of the proposed approach, displaying improved accuracy and reduced false positive rates. Using Grid search CV contributes to optimizing the hyper parameters of the models, further improving their performance. Overall, this research highlights the potential of meta-learning in enhancing intrusion detection systems and provides valuable insights for selecting appropriate algorithms and configurations to improve network security.