Skin cancer poses a significant threat to human health, particularly when early identification and accurate diagnosis are lacking. Early detection is pivotal for successful treatment, given the potential lethality of this disease. While skin cancer is commonly associated with sun-exposed areas, it can also manifest on regions shielded from sunlight. In 2020, there were 150,000 new melanoma cases reported worldwide, making it the 17th most prevalent form of cancer. With a rapid increase in skin cancer cases, there is an urgent need for early and precise differentiation between cancerous and non-cancerous lesions. In response to this critical demand, we propose an advanced model for region-based skin cancer lesion classification utilizing the Faster CNN architecture. This innovative model aims to revolutionize skin cancer diagnosis, reducing reliance on dermatologists’ experience and conventional diagnostic tools. Our study presents an efficient automated system with enhanced evaluation and accuracy metrics, surpassing both prior research and expert dermatologists, achieving an impressive 92% accuracy for the proposed region-based Faster CNN model. This research opens the door to a more effective approach to skin cancer classification and early diagnosis.