In the context of network traffic analysis, this review study offers a thorough examination of the integration of deep learning and evolutionary algorithms for intrusion detection system (IDS) optimization. The importance of network security and the shortcomings of traditional intrusion detection systems are examined. The study emphasizes the possible advantages of integrating evolutionary algorithms for feature selection and input layout optimization with deep learning methods like Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The goals and research gaps are described, as well as the suggested technique. The anticipated outcomes—better accuracy, lower false alarm rates, better zero-day attack detection, and useful recommendations for network security experts—are given. This survey paper aims to contribute to the field of network security by providing a comprehensive overview of the state-of-the-art approaches and identifying future research directions.