The research "Detection of Skin Cancer Types Using Deep Learning" addresses the serious issue of skin cancer. There's an urgent need for early diagnosis to help patients get better treatment. Skin cancer, especially melanoma, can be hazardous and often leads to high death rates when not caught early. Traditionally, doctors mainly rely on visual checks, which can vary from person to person. This can lead to misdiagnoses and delayed treatments. So, we decided to use a technology called Convolutional Neural Networks (CNNs) to create a machine that recognizes different types of skin cancer using specialized images. We did a thorough review of current methods and identified their limitations. This will help us build our approach while also making it easier for places with fewer resources to access. By studying things like color, texture, shape, and size in dermoscopy images—and using fresh techniques like transfer learning—we hope to boost accuracy and efficiency in diagnosis. Ultimately, we look forward to helping improve skin cancer treatments.