This chapter explores the application of supervised and unsupervised learning in real-world scenarios. In supervised learning, two case studies are presented: personal credit evaluation and divorce rate prediction. For credit evaluation, a regression model is developed to predict an individual’s credit score based on five key factors, such as payment history and employment status, by training a machine learning model with historical data. For divorce prediction, a classification approach is used, where Gottman’s emotional interaction scores of couples are analyzed to predict the likelihood of divorce. In unsupervised learning, three applications are discussed: anomaly detection, user segmentation, and recommendation systems. Unsupervised learning is particularly effective in detecting unusual patterns, such as fraud, by clustering user behavior data. It is also used in advertising platforms for segmenting users based on demographics, allowing for targeted ads, and in recommendation systems, where user behavior data is clustered to suggest relevant products. These applications highlight the versatility of both supervised and unsupervised learning methods across diverse industries.