Zero-Shot Learning is a machine learning approach that enables models to recognize and classify objects or concepts they have never seen before. Instead of relying solely on labeled training data, it uses knowledge from related categories to make predictions. This is particularly useful in situations where obtaining labeled data is difficult or expensive. In Zero-Shot Learning, models leverage semantic information, such as attributes or descriptions, to understand new classes. For example, if a model knows about dogs and cats, it can identify a zebra by understanding its characteristics, even if it has never encountered one during training.