This paper explores the application of deep learning technologies in the detection and diagnosis of skin cancer, leveraging advanced convolutional neural networks (CNNs) such as VGG-16, ResNet50, and MobileNetV2. Given the increasing incidence of skin cancer worldwide, there is a critical need for more accurate and efficient diagnostic methods. Our study employs transfer learning to enhance the capability of these networks, enabling them to detect skin cancer from dermoscopic images with a high degree of accuracy, often surpassing human expert performance. We detail the process of data collection, preprocessing, model training, and evaluation, emphasizing the adaptation of these models to skin cancer detection. The results demonstrate significant improvements in diagnostic accuracy, with MobileNetV2 showing notable efficiency in processing and resource utilization—qualities that are ideal for real-time diagnostic applications. In MobileNetV2 we achieved 97% accuracy. Furthermore, the study addresses the challenges of adversarial attacks and the integration of non-image data to refine diagnostic procedures. Our findings highlight the transformative potential of AI in medical diagnostics, suggesting that deep learning could become a cornerstone in the early detection and treatment of skin cancer, thereby improving patient outcomes substantially.