Skin cancer is a fatal disease, requiring an automated mechanism for early and accurate diagnosis. In this paper, an approach utilizing the power of Swin Transformers, a deep learning architecture, to detect skin cancer has been proposed. Swin Transformers are recognized for their ability to capture complex patterns and dependencies in high-resolution images. Considering this aspect a swin transformer based model with a transfer learning approach has been proposed to detect malignancy of skin lesions. Transfer learning is used to enhance accuracy and helps for faster convergence. The proposed model utilizes fine-tuning of pre-trained Swin Transformer models on dermatoscopic images and achieves an accuracy of 94% specifying its ability to differentiate benign and malignant skin lesions. The proposed technique can detect the occurrence of skin cancer at an earlier stage and supports healthcare professionals in their decision-making processes.