Skin cancer is one of the most dangerous diseases in the world. As per the American Cancer Society, about 99,780 new melanomas will be diagnosed in the United States and about 7,650 people are expected to die of melanoma. Correctly classifying skin lesions at an early stage could aid clinical decision-making by providing an accurate disease diagnosis, potentially increasing the chances of cure before cancer spreads. Artificial intelligence (AI) has wide applications in healthcare, including dermatology. Machine learning (ML) is a subfield of AI involving statistical models and algorithms that can progressively learn from data to predict the characteristics of new samples and perform a desired task. AI can be of use for the early detection of skin cancer. For example, the use of deep convolutional neural networks can help to develop a system to evaluate images of the skin to diagnose skin cancer. However, there is likelihood of errors and it depends on a lot of factors, including the amount and quality of the data used to train the algorithms, the environment in which machine learning operates may itself evolve or differ from what the algorithms were developed to face, the complexity of the overall systems it's embedded in. While, in the deep learning approach the knowledge is developed based on sample picture, there are rule based traditional approach which has a business logic built in the application. Business logics could be based on teat results, skin colors, etc. This paper presents a comprehensive hybrid approach combining the AI methodology along with the color pigment analysis to reduce the errors and improve the accuracy and a discussion on how it can be implemented in the field of diagnosing skin cancer. We reviewed the latest research and key discoveries in ML encompassing various subfields of dermatology related cancers. Literature review was performed to screen the articles published in "Skincancer.org, mdpi.com, iopscience.iop.org, hbr.org, medium.com, PubMed and Google Scholar" through August 2022. The search words included "Artificial intelligence AND skin cancer" "Machine learning AND skin cancer" and "Deep learning AND skin cancer". Relevant references of the screened articles were also included for qualitative analysis. Important websites related to skin cancer and related AI resources were also browsed to gather information on the topic. Types of skin cancer: Skin cancer is the out-of-control growth of abnormal cells in the epidermis, the outermost skin layer, caused by unrepaired DNA damage that triggers mutations. These mutations lead the skin cells to multiply rapidly and form malignant tumors. The main types of skin cancer are basal cell carcinoma (BCC), squamous cell carcinoma (SCC), melanoma and Merkel cell carcinoma (MCC). The two main causes of skin cancer are the sun's harmful ultraviolet (UV) rays and the use of UV tanning beds.