This paper provides a comprehensive survey of meta-learning approaches for domain generalization, focusing on their contributions to enhancing the ability of models to generalize across unseen domains. Deep neural networks (DNNs) often struggle with out-of-distribution (OOD) data due to the common assumption that training and testing data share the same distribution, which is frequently violated in real-world applications. Meta-learning offers a promising solution by enabling models to acquire transferable knowledge across various tasks, facilitating fast adaptation and eliminating the need to learn each task from scratch. The paper begins by clarifying the concept of meta-learning for domain generalization and introduces a novel taxonomy based on feature extraction strategies and classifier learning methodologies. This taxonomy helps in understanding the granular details of different meta-learning approaches. Additionally, a decision graph is presented to assist readers in selecting appropriate models based on data availability and domain shifts. The survey reviews existing methods and underlying theories, mapping out the fundamentals of the field. It highlights practical insights and discusses promising research directions. Key challenges and open questions are also identified, providing a comprehensive reference for researchers and practitioners interested in advancing the field of meta-learning for domain generalization. Meta-learning for domain generalization aims to enhance the model's ability to handle distributional shifts and improve generalization across various domains. The paper discusses different learning paradigms, including incremental learning, online learning, continual learning, transfer learning, multi-task learning, and meta-learning. It emphasizes the importance of understanding causal relationships within data and the role of meta-learning in learning how to learn, leveraging knowledge from previous tasks to quickly adapt to new tasks with minimal data. The formalization of meta-learning for domain generalization is discussed, highlighting the goal of solving new unseen tasks by leveraging knowledge from previous tasks. The paper introduces a taxonomy with two axes: the generalizability axis and the discriminability axis, which represent the strategies of the feature extractor and the classifier training process, respectively. This taxonomy helps in categorizing different meta-learning approaches and understanding their mechanisms for promoting generalization. The paper then explores various meta-learning methods for domain generalization, including MLDG, MetaReg, Feature-Critic Networks, episodic training, meta-learning the invariant representation, MASF, S-MLDG, MetaVIB, and M-ADA. Each method is described in detail, along with its strengths and weaknesses. The paper concludes by summarizing the main points and their implications, providing a comprehensive overview of the current state and future directions in meta-learning for domain generalization.This paper provides a comprehensive survey of meta-learning approaches for domain generalization, focusing on their contributions to enhancing the ability of models to generalize across unseen domains. Deep neural networks (DNNs) often struggle with out-of-distribution (OOD) data due to the common assumption that training and testing data share the same distribution, which is frequently violated in real-world applications. Meta-learning offers a promising solution by enabling models to acquire transferable knowledge across various tasks, facilitating fast adaptation and eliminating the need to learn each task from scratch. The paper begins by clarifying the concept of meta-learning for domain generalization and introduces a novel taxonomy based on feature extraction strategies and classifier learning methodologies. This taxonomy helps in understanding the granular details of different meta-learning approaches. Additionally, a decision graph is presented to assist readers in selecting appropriate models based on data availability and domain shifts. The survey reviews existing methods and underlying theories, mapping out the fundamentals of the field. It highlights practical insights and discusses promising research directions. Key challenges and open questions are also identified, providing a comprehensive reference for researchers and practitioners interested in advancing the field of meta-learning for domain generalization. Meta-learning for domain generalization aims to enhance the model's ability to handle distributional shifts and improve generalization across various domains. The paper discusses different learning paradigms, including incremental learning, online learning, continual learning, transfer learning, multi-task learning, and meta-learning. It emphasizes the importance of understanding causal relationships within data and the role of meta-learning in learning how to learn, leveraging knowledge from previous tasks to quickly adapt to new tasks with minimal data. The formalization of meta-learning for domain generalization is discussed, highlighting the goal of solving new unseen tasks by leveraging knowledge from previous tasks. The paper introduces a taxonomy with two axes: the generalizability axis and the discriminability axis, which represent the strategies of the feature extractor and the classifier training process, respectively. This taxonomy helps in categorizing different meta-learning approaches and understanding their mechanisms for promoting generalization. The paper then explores various meta-learning methods for domain generalization, including MLDG, MetaReg, Feature-Critic Networks, episodic training, meta-learning the invariant representation, MASF, S-MLDG, MetaVIB, and M-ADA. Each method is described in detail, along with its strengths and weaknesses. The paper concludes by summarizing the main points and their implications, providing a comprehensive overview of the current state and future directions in meta-learning for domain generalization.