Skin cancer is one of the most current forms of cancer, and early discovery is critical for effective treatment. This paper presents across-dataset dermatological image bracket frame designed to enhance conception and robustness. The proposed system leverages a convolutional neural network (CNN) armature with preprocessing, data addition, and transfer literacy ways. Training is conducted on the HAM10000 dataset, while evaluation is performed on a test dataset composed of the ISIC 2019 dataset and 15 of HAM10000. Original trials using a VGG16- grounded model revealed overfitting issues, reducing test delicacy from 70 to 62. To address this, colourful optimization strategies were enforced, leading to bettered results with a final model achieving 84.2 delicacy on HAM10000 and 76.5 on ISIC. These findings punctuate the significance of dataset diversity, careful preprocessing, and architectural advancements for effective skin lesion bracket. Unborn exploration will explore advanced models and ensemble ways to further ameliorate performance and support real-world clinical operations.