Skin cancer is one of the most common and deadliest types of cancer, with its incidence rapidly increasing worldwide. Failure to diagnose it in its early stages can lead to metastasis and high mortality rates. This study provides a systematic review of recent literature on the application of traditional machine learning and deep learning in the diagnosis of skin cancer lesions, providing valuable reference for further research in skin cancer diagnosis. Firstly, several publicly available datasets of skin diseases are compiled. Secondly, the application of different machine learning algorithms in the classification of skin cancer lesions is analyzed and compared to better understand their advantages and limitations in practical applications, with a focus on convolutional neural networks in diagnosis classification. With a thorough understanding of these algorithms, their performance differences and improvement strategies in dealing with skin diseases are discussed. Ultimately, through discussions on current challenges and future directions, beneficial insights and recommendations are provided to further enhance the performance and reliability of early skin cancer diagnosis systems.