Skin disorders are common across the globe, often proving to be difficult to diagnose because of coexisting signs and symptoms. In this paper, we study the feasibility of using machine learning (ML) techniques for automatic skin disease detection. We look at the emerging patterns in fundamental studies within the scope of focus that deals with image processing for feature extraction and employing classification methods for disease detection. We focus on feature extraction and the classification of images. One of the major strengths is the ML-based approach with better access and usability and higher chances of them being detected at an early stage. In addition, we consider some of the drawbacks and problems of these methods, including biased data and lack of sufficient professional oversight. We also consider other aspects, whereby one of them is further analysis of the requirement in the case of the absence of the adequate data, standard models, and unambiguous explanations of the inner processes.