Skin cancer is among the most widespread and fastest-growing cancers globally, posing a significant public health concern. Detecting skin cancer early and accurately is crucial to improving patient outcomes. In this review, we discuss the current methods used for skin cancer detection, with a special focus on deep learning techniques. We highlight recent advances in deep learning techniques, how they are used in practice, and how they’ve helped enhance the efficiency and accuracy of diagnoses. In particular, we examine key datasets used in skin cancer research, the role that deep learning plays in prognosis, and the challenges that still need to be addressed in this field. Finally, we share our thoughts on future directions and the potential for deep learning to further revolutionize skin cancer detection and classification. This review aims to give researchers and clinicians a clear overview of the progress made so far and the future steps needed to tackle this global health problem.