Skin cancer is a common and serious type of cancer, with melanoma having the highest fatality rate. Early detection and treatment can significantly improve the survival rate of skin cancer patients. Dermoscopic, macroscopic, and histopathological images all play essential roles in diagnosis. The application of artificial intelligence technology can effectively enhance the efficiency of classifying these three types of images and help reduce diagnostic costs. Deep learning, with its feature extraction capabilities, is more suitable for the classification tasks of detailed skin cancer images. This study reviews the relevant research on the classification tasks of the three types of images commonly used in skin cancer diagnosis, analyzes the technical focuses of the three types of images due to their different image characteristics, and conducts targeted analysis of the difficulties faced in clinical application. Finally, future developments and challenges are discussed to promote the broader application of artificial intelligence in skin cancer diagnosis.