Melanoma is the deadliest type of skin cancer that needs to be detected at its early stages to prevent fatality. Using dermoscopy images of the lesion a computer-based system trained with deep learning will be developed to detect melanoma. The model will identify and categorize melanoma with intricate image processing and classification algorithms, which will be trained on a labeled dataset. Some of the goals of this project are to compile and preprocess a dataset of dermoscopy images labeled with benign lesions and melanoma, evaluate using metrics such as AUC-ROC, accuracy and validation with external datasets, addressing bias while following clinical guidelines. At the end of this research, we hope to improve patient outcomes and lessen the cost of healthcare, making it affordable as well as increasing diagnostic accuracy, decreasing false positives, and assisting dermatologists in the early detection of the disease.