Few-shot Learningfew-shot learning learningFew-shotfew-shot learning enables machine learningLearningmachine learning models to rapidly adapt to new tasksTask using only a small number of training examples, addressing challenges where data is scarce or rapid generalizationGeneralization is required. This chapter provides a comprehensive overview of few-shot Learningfew-shot learning learningFew-shotfew-shot learning methodologies, encompassing both episodicEpisodeepisodic and non-episodic approaches, with an emphasis onMeta-learning meta-learningLearningmeta-learning as a prominent framework for episodic Episodeepisodic few-shot Learningfew-shot learning learningFew-shotfew-shot learning. Key concepts such as episodes, tasksTask, and benchmark datasetsDatadataset are introduced to ground the discussion. Additionally, the chapter explores the challenges posed by domain shifts between training and testing data distributionsDistribution, highlighting transfer learning, domain adaptation, and domain generalizationDomain generalization as strategies to enhance model robustness. The role ofMeta-learning meta-learningLearningmeta-learning in enabling domain generalizationDomain generalization is examined through algorithms such as Model-Agnostic Meta-LearningLearningmeta-learning (MAML)Meta-learning andModel agnostic meta-learning (MAML) Model-Agnostic Learning of Semantic Features (MASF). This chapter aims to equip readers with both foundational knowledge and advanced techniques for few-shot Learningfew-shot learning learningFew-shotfew-shot learning and its applications in dynamic, low-data environments.