Treatment and management of skin diseases depend on quick and accurate diagnosis. Laboratory testing and clinical evaluation are frequently used in traditional diagnosis techniques, which can be laborious and prone to human error. Skin diseases deteriorate when the proper treatments are not administered. Because dermatologist visits are costly, the illness is frequently neglected, which makes it more problematic. The diagnosis and classification of skin diseases can be automated due to developments in Deep Learning (DL) and Artificial Intelligence (AI) in recent years. In this study, 23 different skin diseases are separated using deep learning algorithms applied to image data. In order to assess and classify images of skin lesions, this method uses convolutional neural networks (CNNs), which may be more accurate than more traditional diagnostic methods. The efficacy of deep learning in dermatological diagnostics and its potential to support medical professionals in clinical settings is highlighted in this paper’s discussion of the technique, dataset, training procedure, and outcomes.