Intrusion Detection Systems (IDS) are critical for safeguarding network environments against the increasing complexity and variety of cyber threats. This review paper evaluates the deep learning methodologies applied to the Communications Security Establishment of the Canadian Institute for Cybersecurity (CSE-CIC-IDS2018) dataset, a comprehensive benchmark for network intrusion detection. This study systematically examines the strengths and limitations of models, such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and hybrid approaches combining multiple deep learning techniques. Our analysis highlights that hybrid models, particularly those integrating CNN and LSTM architectures, consistently outperform others in terms of accuracy, precision, recall, and F1 score, achieving performance metrics as high as 99.89%. Despite these successes, challenges persist, such as overfitting, computational inefficiency, and handling novel attacks. This paper also discusses the necessity of more diverse and up-to-date datasets and suggests future research directions, including the incorporation of transfer learning, reinforcement learning, and improved dataset diversity to enhance the robustness and real-world applicability of IDS models. Ultimately, this review underscores the critical role of deep learning in advancing the efficacy of IDS in the face of evolving cybersecurity threats.