<p>Skin cancer is one of the most life-threatening medical conditions worldwide, and its timely and accurate diagnosis plays a crucial role in improving patient survival rates. Deep learning algorithms have made significant strides recently, demonstrating promising outcomes in image classification and segmentation of skin cancer. This review presents an extensive discussion of deep learning architectures and their applications in skin cancer identification. Specifically, it disseminates the public benchmark datasets for skin cancer detection. The paper then explores widely adopted deep learning architectures and highlights their strengths for skin cancer detection. Next, it gives a detailed analysis of the state-of-the-art approaches for skin cancer detection, highlighting their architectures, contributions, limitations, and performance on benchmark datasets. This review also presents the software tools and evaluation metrics crucial in skin cancer detection. Finally, the review discusses several open issues and future research directions important for skin cancer detection. It is believed that potential researchers working in the relevant domain can refer to and benefit from this paper.</p>