The field of machine learning is moving towards addressing the challenges of imbalanced data, where the distribution of target variables or classes is skewed. Researchers are proposing innovative solutions to handle imbalanced regression, small sample imbalance problems, and context-aware object detection. A key trend is the development of adaptive oversampling methods, data transformation frameworks, and robust learning techniques that eliminate the need for bias annotations. These approaches aim to improve model performance, reduce spurious correlations, and produce debiased data distributions. Noteworthy papers include Local Distribution-based Adaptive Oversampling, which achieves state-of-the-art results on imbalanced datasets, and Class-Conditional Distribution Balancing, which offers a simple yet effective robust learning method. Additionally, Opposition-Based Data Transformation and Data Augmentation Partial Least Squares Regression models have shown promise in enhancing classification performance and handling uneven categories.