Protecting network security from evolving cyberattacks is a significant challenge for Intrusion Detection Systems (IDS). Traditional IDS are struggled to detect novel and complex attacks successfully, needing innovative ways to improve their efficacy. This study looks into the use of metaheuristic optimization approaches to increase IDS performance, using the NSL-KDD and KDDCup99 datasets as benchmarks. We explore the optimization of machine learning model parameters for intrusion detection using Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Specifically, we evaluate the performance of a Decision Tree (DT) model enhanced using Genetic Algorithms. Our experiments evaluate the prediction model’s performance metrics like accuracy, recall, precision, and the F1 measure. The results show that the DT with GA model outperforms standard methods while drastically reducing model running time. This upgrade demonstrates the efficacy of metaheuristic optimization in adapting IDS to combat sophisticated and dynamic cyber threats.