The evolving digital environment requires sophisticated decision systems to protect important information assets from the growing complexity and variety of cyber threats. This review paper examines the impact of machine learning (ML) on cybersecurity decision systems, with a specific focus on analyzing ML and deep learning (DL) methods. The study employs the UNSW-NB15 dataset, which is a widely used benchmark dataset in the field of network security. The paper starts with an overview of the present cybersecurity environment, emphasizing the difficulties presented by advanced and changing cyber threats. It highlights the importance of adaptive and intelligent decision systems that can efficiently identify and reduce cyberattacks as they occur. A significant part of the paper focuses on a thorough analysis of different machine learning and deep learning techniques used in cybersecurity. The review discusses conventional machine learning algorithms like decision trees, support vector machines, and ensemble methods, along with sophisticated deep learning architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Each approach is thoroughly assessed for its strengths and limitations based on factors like accuracy, interpretability, and scalability. The performance of various ML and DL models is evaluated using the UNSW-NB15 dataset as a benchmark. The dataset covers various cyberattack scenarios, enabling a thorough assessment of algorithms’ ability to detect anomalies and classify malicious activities. The paper explores the incorporation of machine learning (ML) and deep learning (DL) into decision support systems, highlighting the significance of explainability and interpretability in the realm of cybersecurity. The conversation covers the difficulties of implementing machine learning (ML) and deep learning (DL) models in practical situations, such as concerns about data privacy, adversarial attacks, and model resilience. This review paper offers a thorough analysis of decision systems in cybersecurity using a machine learning approach, providing insights into the strengths and weaknesses of different ML and DL methods. Studying the UNSW-NB15 dataset helps assess how well models perform in real-world cyber threat situations, which can inform future research and development of stronger cybersecurity solutions.