This is the final chapter of this book. This chapter focusses on machine learning (ML) models and their application in detecting anomalies or outliers. Identifying such irregular patterns in data is particularly important in domains where deviations from normal behavior can indicate abnormal pattern or fraudulent activity. Fraud detection relies heavily on recognizing anomalies within large datasets. Data mining is a key enabler in effective fraud detection. Effective use of data mining and algorithms are crucial across industries such as finance, insurance, and retail, where early detection of outliers can prevent significant losses. Traditional fraud detection systems rely on rule-based approaches and static thresholds, which fraudsters quickly learn to evade. In this chapter, we introduce ML methods for to develop an adaptive, intelligent fraud detection that automatically learn complex patterns from data. The chapter progresses through data preparation, model architecture design, training procedures, and performance evaluation, providing readers with complete, executable code for building production-grade anomaly detection systems. We experiment and implement different approaches including Isolation Forests, autoencoders, one-class support vector machines (SVMs), local outlier factor methods, etc., and show how to empirically compare their strengths to determine on a specific algorithm. We have discussed fraud detection in detail; however, the same principles can be applied more broadly to identify anomalies, outliers, or irregular patterns in other domains, such as credit risk assessment, supply chain monitoring, or transaction compliance. By leveraging similar statistical and machine learning techniques, these methods can help uncover hidden risks and improve decision-making across diverse applications.