Zero-shot learning (ZSL) is a machine learning paradigm that allows models to recognize and classify objects or tasks that they have not encountered during training. This is achieved by leveraging semantic information, such as textual descriptions or attributes, that relate known classes to unseen classes. For example, if a model has been trained to recognize cats and dogs but receives descriptions of horses, it can identify horses based on the learned attributes without any prior examples. Zero-shot learning is particularly valuable in scenarios where acquiring labeled data is expensive or impractical, allowing AI systems to generalize knowledge and adapt to new challenges more effectively.