Image augmentation has proven to be a cornerstone of successful deep learning models in computer vision. By artificially expanding the training dataset, augmentation techniques enhance model robustness, generalization, and performance. However, the field is continually evolving, with exciting advancements on the horizon. The future of deep learning image augmentation lies in pushing the limits of current methods and exploring novel approaches (Sengupta in Knowledge-Based Systems 194, 2020). This chapter will delve into emerging trends and promising directions, including the integration of generative models, the development of more sophisticated augmentation policies, and the exploration of domain-specific augmentation strategies.