Skin cancer, especially melanoma, remains a major global health concern due to its high mortality when not diagnosed early. Conventional diagnostic methods rely heavily on dermatological expertise, which may not be readily available in all regions. This paper presents an AI-powered system for early detection of skin cancer through dermatoscopic image analysis. The proposed method employs deep learning models-specifically CNNs, ResNet50, EfficientNetB0, and Vision Transformers (ViT)-to classify lesions as benign or malignant. By incorporating advanced preprocessing, transfer learning, and visual explanation techniques like Grad-CAM, the system enhances diagnostic accuracy and interpretability. Evaluated on publicly available datasets (HAM10000 and ISIC), the ViT-based model achieved 95.3% accuracy and a 95.0% F1 score, demonstrating high potential for real-time deployment in telemedicine and clinical settings.