In theIndustry 4.0 IndustryAI-based Anomaly Detection 4.0 era, smart manufacturing has become an integral part of today’s modern industrial space with the integration of advanced technologies such as the Artificial intelligence (AI)Artificial Intelligence (AI), big dataBig Data analytics, and Internet of Things (IoT)Internet of Things (IoT). This chapter provides a comprehensive review of recent advancements in AI-based anomaly detectionAI-based Anomaly Detection techniques tailored for smart manufacturing contexts. Different machine learning (ML)Machine Learning (ML) and deep learningDeep Learning (DL) techniques, including supervised learning, unsupervised learning, semi-supervised learning, reinforcement learningReinforcement Learning, and hybrid learning, explore the applicability of anomaly detection for various types of anomalies, address challenges of class imbalance and the scarcity of labeled data, and adapt to various industrial environments. Leveraging ML and DL methodologies and the large number of multipurpose anomaly benchmark datasets, AI has demonstrated superior accuracy and efficiency in identifying anomalies crucial for maintaining operational efficiency, ensuring product quality, and safeguarding human workers and machinery. Furthermore, the chapter looks into future trends and potential improvements in AI-based anomaly detectionAI-based Anomaly Detection, considering the challenges for integrating the AI systems into existing infrastructure. By addressing these challenges, AI-based anomaly detection can significantly enhance the reliability and productivity of smart manufacturing systems.