Domain generalization (DG) aims to enable models to generalize well to out-of-distribution (OOD) data without access to target domain data. This is challenging for machines due to the i.i.d. assumption in most learning algorithms, which is often violated in practice due to domain shift. DG has been studied in various fields, including computer vision, speech recognition, natural language processing, medical imaging, and reinforcement learning. This survey provides a comprehensive review of DG methodologies, including domain alignment, meta-learning, data augmentation, and ensemble learning. The paper discusses the problem definition, evaluation metrics, related topics, and current research directions. It highlights the importance of domain generalization in real-world applications where target data is unavailable or difficult to obtain. The survey also covers various datasets and applications, such as handwritten digit recognition, object recognition, semantic segmentation, person re-identification, face recognition, and speech recognition. The paper emphasizes the need for robust models that can handle domain shift and generalize well to unseen data distributions. It also discusses the challenges and limitations of existing DG methods and suggests future research directions.Domain generalization (DG) aims to enable models to generalize well to out-of-distribution (OOD) data without access to target domain data. This is challenging for machines due to the i.i.d. assumption in most learning algorithms, which is often violated in practice due to domain shift. DG has been studied in various fields, including computer vision, speech recognition, natural language processing, medical imaging, and reinforcement learning. This survey provides a comprehensive review of DG methodologies, including domain alignment, meta-learning, data augmentation, and ensemble learning. The paper discusses the problem definition, evaluation metrics, related topics, and current research directions. It highlights the importance of domain generalization in real-world applications where target data is unavailable or difficult to obtain. The survey also covers various datasets and applications, such as handwritten digit recognition, object recognition, semantic segmentation, person re-identification, face recognition, and speech recognition. The paper emphasizes the need for robust models that can handle domain shift and generalize well to unseen data distributions. It also discusses the challenges and limitations of existing DG methods and suggests future research directions.