Skin cancer is characterized by the uncontrolled proliferation of abnormal cells in the outermost skin layer, the epidermis, due to unrepaired DNA damage leading to mutations. These mutations cause rapid multiplication of skin cells, forming malignant tumors. The primary types of skin cancer include basal cell carcinoma (BCC), squamous cell carcinoma (SCC), melanoma, and Merkel cell carcinoma (MCC). Melanoma of the skin ranks as the 17th most common cancer worldwide, with more than 150,000 new cases reported in 2020. Early detection and treatment of melanoma can significantly impact patient outcomes. The present work aims to detect melanoma skin cancer in its early stages using image processing through Computer Vision and deep learning methodologies. The culmination of this effort is an Android application designed to facilitate self-diagnosis for users, offering timely alerts on when to consult a medical professional. Hospitals can also utilize the application to prioritize patient care based on their risk percentages, benefiting both patients and healthcare providers. The study delves into relevant research papers published in esteemed journals related to skin cancer diagnosis. Deep learning methods are proposed to assist dermatologists in achieving early and accurate diagnoses. While specialists can provide accurate diagnoses, the development of automated systems becomes crucial to efficiently diagnose diseases, saving lives and reducing healthcare and financial burdens. Machine learning (ML) emerges as a valuable tool in this context. The article focuses on the fundamentals of ML and its potential in aiding skin cancer diagnosis. The objective is to conduct a comparative study between the DenseNet-121, ResNet-50, and CNN-RF models. The study reveals that DenseNet-121 outperformed with a testing accuracy of 83%, surpassing ResNet- 50, which achieved 81% testing accuracy. This comparative analysis contributes to the ongoing research and development in the field of skin cancer diagnosis.