One-shot learning is a rapidly growing area in machine learning and deep learning that focuses on enabling models to recognize and classify new classes from a single labeled example, addressing the challenges of data scarcity and generalization. Research in this field extensively explores meta-learning approaches, such as model-agnostic meta-learning (MAML), prototypical networks, and relation networks, which aim to learn transferable knowledge across tasks to facilitate fast adaptation. Additionally, metric-learning techniques, including Siamese networks and triplet networks, are widely employed to learn similarity measures between examples, improving recognition of novel classes. Recent studies also incorporate attention mechanisms, memory-augmented networks, and generative models to augment limited data and enhance feature representations. Applications of one-shot learning span computer vision tasks like object and character recognition, natural language processing for intent detection and entity recognition, speech recognition, and medical imaging, demonstrating its importance in scenarios where collecting large labeled datasets is impractical while maintaining robust performance on unseen categories.