A Comprehensive Review of Skin Disease Detection and Classification Using Machine Learning and Deep Learning Techniques

Skin diseases are among the most common health concerns worldwide, with timely and accurate diagnosis playing a critical role in effective treatment and patient outcomes. Recent advancements in Artificial Intelligence, particularly in Machine Learning and Deep Learning have shown immense promise in the automated detection and classification of skin conditions. This review provides a comprehensive analysis of existing ML and DL techniques used for skin disease diagnosis, covering classical algorithms such as Support Vector Machines, k-Nearest Neighbors, and Random Forests, as well as state-of-the-art Convolutional Neural Networks, including ResNet, VGG, Inception, and DenseNet. We also explore the role of transfer learning, attention mechanisms, and ensemble models in enhancing diagnostic performance. Moreover, we discuss current challenges including data scarcity, generalization, interpretability, and ethical concerns. Real-world applications such as mobile diagnostic apps, telemedicine integration, and deployment in remote healthcare settings are also examined. This review concludes by underscoring the transformative potential of AI in dermatology and calls for stronger interdisciplinary collaboration between clinicians and data scientists to develop robust, ethical, and scalable diagnostic systems.