Machine learning models make predictions by utilizing statistical techniques to recognize patterns in data. These models are trained on historical datasets, where they learn to identify correlations between input features and the desired output. Through algorithms like regression or classification, models generate predictions based on new, unseen data. As they process more data over time, machine learning models improve their accuracy and generalizability, enabling applications ranging from recommendation systems to predictive analytics and beyond, thereby enhancing decision-making processes.