Machine learning plays a pivotal role in recommendation systems, as it uses algorithms to predict user preferences based on historical data. By analyzing past behaviors, preferences, and interactions, machine learning models can identify patterns that help tailor personalized recommendations to users. This is commonly seen in platforms like streaming services and e-commerce websites, enhancing user experience and engagement. Techniques such as collaborative filtering and content-based filtering are employed to suggest products or content that are most likely to appeal to an individual, thereby driving sales and user satisfaction.