Zero-shot learning is an AI paradigm that allows models to make inferences and generalizations without prior exposure to specific training data, thereby testing their ability to process new information on the fly. This method is not a replacement for traditional training but rather an enhancement to pre-trained models, enabling them to tackle unfamiliar tasks effectively. Zero-shot learning finds applications across various industries, including healthcare for diagnosing rare conditions, pharmaceuticals for predicting compound efficacy, and natural language processing for understanding new slang or issues. It contrasts with other learning paradigms like one-shot and few-shot learning by eliminating the need for labeled examples, relying instead on pre-training and reasoning to identify and categorize unknown inputs. While this approach is powerful, it presents challenges such as potential hallucinations and the high costs associated with extensive pre-training data requirements, which are mitigated by techniques like generalized zero-shot learning (GZSL) and prompt engineering. As AI continues to evolve, zero-shot learning is expected to play an increasingly vital role in enabling models to adapt to diverse and dynamic environments.