This paper presents a comprehensive review of over 50 articles on anomaly detection techniques, emphasizing the distinctions between traditional machine learning approaches and the emerging deep learning methods. Machine learning algorithms, such as support vector machines and decision trees, have long been utilized for anomaly detection but often struggle with complex, high-dimensional data. In contrast, deep learning techniques, including convolutional neural networks and autoencoders, offer enhanced capabilities in feature extraction and representation, making themmore suitable for detecting subtle and intricate anomalies in large datasets. The review covers a wide range of application areas where artificial intelligence plays a critical role in anomaly detection, including cybersecurity, industrial automation, healthcare, and the Internet of Things (IoT). By analysing the strengths and limitations of different methods, the paper highlights the growing importance of deep learning in addressing challenges like real-time detection, scalability, and adaptability to diverse data types. This review provides valuable insights for researchers and practitioners, offering a roadmap for future developments in the field of anomaly detection using artificial intelligence.