Data augmentation is a critical technique for overcoming data scarcity and imbalances in many machine learning and deep learning applications. This review presents a systematic analysis of data augmentation methods with a focus on their application in decision support systems within management, economics, and finance. Terminological discrepancies between data transformation and augmentation were also taken into account, emphasizing the importance of properly distinguishing between these concepts for the accurate interpretation of the applied methods. Both traditional and advanced augmentation strategies are discussed, including geometric transformations, generative models (GANs, cGANs, VAEs), oversampling techniques such as SMOTE and ADASYN, and the Mixup approach, highlighting their benefits in improving model generalization and accuracy. Challenges related to automation and reproducibility are also addressed, as well as suggestions for future research directions. Additionally, the review explores how classical machine learning models. Especially in structured data contexts -benefit from augmentation techniques such as SMOTE, bootstrapping, and noise injection.