<p>Most machine learning algorithms typically assume that the data distribution of the training and test sets are consistent, but this assumption often fails to hold in practical applications. Domain generalization aims to train a model using only available source data so that the model can generalize to unseen domains. Data augmentation is an important technique in domain generalization, but there are few comprehensive reviews investigating and summarizing its use in domain generalization. This study provides a comprehensive literature review of data augmentation methods in domain generalization for the first time. First, we formalize the definition of domain generalization and analyze the role of data augmentation in domain generalization. Second, we propose a new taxonomy that categorizes methods into three classes based on the augmentation objectives: domain-level, image-level, and feature-level augmentation. Third, we compare the experimental results of some data augmentation methods on three popular domain generalization datasets and discuss the characteristics and advantages of the current best methods. Fourth, we analyze the shortcomings of each category, propose the suggestions for improvements, and summarize the challenges and the future directions of data augmentation for achieving cross-domain generalization from both theoretical and practical perspectives.</p>