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Skin cancer, now the fifth most common cancer globally, presents a significant health and economic burden. Environmental changes, industrialization, and genetic factors have all contributed to its rising incidence. Early detection and accurate diagnosis are crucial to improving treatment outcomes and reducing mortality rates. In recent years, AI-based techniques, particularly machine learning (ML) and deep learning (DL), have emerged as promising tools for the early detection and diagnosis of skin cancer. These methods have shown impressive results in analyzing medical imaging data, particularly skin lesion images, but challenges remain due to the complexity of the images. This study reviews recent advancements in ML and DL techniques, including convolutional neural networks (CNNs), highlighting their potential in automated skin cancer diagnosis. While these models have demonstrated strong performance, However, major challenges remain, such as limited dataset sizes, class imbalances, and difficulty in generalizing models to diverse populations. CNN models also suffer from interpretability issues, hindering their clinical adoption. There is a gap between research and clinical integration, and models often struggle with adaptability across different environments. Lastly, concerns about data privacy and security persist, requiring robust solutions. Collaborative efforts between computer scientists, clinicians, and medical imaging experts are vital to addressing these issues. The review explores strategies like hybrid models, optimization algorithms, data augmentation, transfer learning, and clinical feature integration to improve classification accuracy. The study compares popular datasets and provides insights from previous research, offering suggestions for future directions to further refine AI-based skin cancer detection systems and address existing limitations.