Aortic dissection (AD) is a critical emergency, where rapid and accurate diagnosis and treatment significantly influence the prognosis of AD patients. However, achieving such precise and swift diagnosis and treatment poses substantial challenges. In recent years, the application of deep learning based on CT images in assisting the diagnosis and treatment of AD has increasingly gained prominence, particularly in the identification, segmentation, and prediction of patient outcomes, thereby offering significant value for clinical decision-making in AD management. To better understand the application of deep learning based on CT images in AD, this review will summarize and discuss the current applications, existing challenges, and potential optimization strategies of deep learning in the diagnosis and treatment of AD.