AI plays a pivotal role in anomaly detection within cybersecurity, essential for identifying irregular patterns indicative of potential threats. Traditional rule-based methods often struggle to keep pace with evolving attack techniques, making AI-driven approaches, particularly machine learning algorithms, indispensable. These AI techniques, including supervised, unsupervised, and semi-supervised learning, excel at analyzing vast datasets for real-time anomaly detection, adapting to changing patterns and uncovering subtle anomalies efficiently. Challenges such as data quality and interpretability persist, but AI's applications span network intrusion detection, fraud detection, IoT security, and more. Future directions involve integrating AI with cybersecurity technologies for robust defense mechanisms. AI in anomaly detection promises to revolutionize cybersecurity, empowering organizations to better protect against emerging cyber threats.