Few-shot learning is a transformative approach in AI and machine learning that involves training algorithms on small datasets, offering a solution for scenarios with limited data. It is widely applied across various real-world domains, including robotics, personalized technology, pharmaceuticals, language processing, and image recognition. Few-shot learning is part of the broader n-shot learning family, which includes zero-shot and one-shot learning, and it enables models to generalize from small amounts of data by leveraging prior knowledge, task-specific adaptation, and generalization. Techniques such as transfer learning, data augmentation, meta learning, and metric learning help address the challenges of generalization, data diversity, and feature representation inherent in few-shot learning. Despite these hurdles, few-shot learning remains a critical advancement, offering efficient means to train AI models without the need for extensive datasets.