In the current global context, people face the risk of losing their lives to various fatal diseases. One significant concern is the potential impact of cellular network radiation and mobile phone usage on health, particularly in relation to cancer. Cancer is characterized by abnormal and uncontrolled tissue growth within the body, with the possibility of spreading to other parts beyond its point of origin. Skin cancer stands out as one of the most perilous illnesses globally. Accurate identification of skin lesions in the early stages can significantly assist clinical judgment by offering precise disease diagnoses, potentially enhancing the likelihood of a cure before cancer spreads. Nevertheless, the unbalanced and finite availability of skin disease images utilized for training presents difficulties in automating skin disease classification. Additionally, ensuring the model’s adaptability across different domains and its robustness are crucial hurdles. Recent advancements have witnessed the widespread application of deep learning-based methods in skin cancer classification to address these challenges and achieve promising outcomes. Despite these efforts, comprehensive reviews encompassing the forefront issues in skin cancer classification remain scarce. This paper aims to fill this gap by providing a thorough overview of the latest algorithms for skin cancer classification based on deep learning.