Skin diseases are a significant healthcare concern, and accurate diagnosis is crucial. Traditional methods, where dermatologists visually assess skin conditions, can be inconsistent. Considering the challenges in skin disease diagnosis, there is a need for automated and objective approaches. Predictions of skin diseases can be made more accurate by using techniques based on machine learning (ML), including support vector machines, decision trees, neural networks, random forests, and deep learning. To improve these models, proper data preparation and feature selection are essential. Using imaging technologies such as dermoscopy and digital photography alongside ML may improve the diagnoses. However, there are limitations, including the need for diverse, curated datasets, potential biases in training data, interpretability of complex models, and challenges in clinical practice. By addressing these limitations, one can use ML more effectively and to its full potential to improve skin disease diagnosis. This chapter summarizes the current situation, challenges, and prospects, encouraging further research and collaboration.