Skin diseases are a significant concern for public health, demanding accurate diagnosis for effective treatment. However, traditional diagnostic methods often suffer from subjectiv ity, invasiveness, and resource intensiveness. In recent years, computer vision techniques have emerged as promising solutions, offering auto- mated tools for skin disease diagnosis that can improve accuracy, efficiency, and accessibility. In this paper, we provide a com prehensive review of computer vision approaches for the automated diagnosis of various skin diseases. We delve into image preprocessing techniques, feature extraction methods, classification algo- rithms, and evaluation metrics commonly employed in this domain. Additionally, we examine how computer vision is applied in specific skin diseases, such as melanoma, psoriasis, eczema, and fungal infections. We also discuss the challenges faced in this field and suggest future directions for research. In summary, this paper highlights how computer vision holds the potential to transform the land- scape of skin disease diag nosis, emphasizing the continued necessity for research and innovation in this domain.