Predictive models use historical data to identify patterns and relationships among variables. This process involves data collection, data preprocessing, model selection, training, validation, and deployment. Techniques like regression analysis, decision trees, neural networks, and clustering are commonly used. The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1 score to ensure reliability.