One of the worst types of cancer, skin cancer can spread to other body parts if not found and treated promptly. Dermoscopy pictures have been at the vanguard of a paradigm shift in medical management: the application of deep learning for skin cancer screening. This work assesses how well Transformer-based deep learning models work for automated skin lesion classification. Our suggested Transformer-based model outperformed conventional CNN architectures such as AlexNet and YOLOv8 with an accuracy of X per cent. Comparative studies reveal enhanced feature extraction and classification performance in dermatological image diagnosis. This paper also addresses issues including dataset biases and the demand for explainability in AI-driven diagnostics, therefore highlighting their importance for actual clinical use.