Skin diseases are most common health issues spread worldwide and require accurate and timely diagnosis for effective treatment. Early, more subjective, more time-consuming manual, visual inspections are the basic forms of examinations made by a dermatologist for early diagnosis. Emergence of new technologies presents new prospects in improving efficiency and accuracy of diagnostics in the dermatology practice. The main objective of this study is to improve skin disease detection with deep learning models, particularly Convolutional Neural Networks (CNN) and Residual Networks (ResNet), based on the large, comprehensive Dermnet dataset, containing a huge variety of dermoscopic images representing numerous skin diseases. A more robust preprocessing procedure along with the configuration of a proper data augmentation technique are additional strategies to be used to make stable models. By design, the CNN model, which is recognized as the model that captures spatial hierarchies with the highest precision, got an accuracy of 89.3%. As opposed to CNN, ResNet, which uses residual learning to address the gradient vanishing problem, way outperformed it with an accuracy of 91.7%. With the results pointing that not only CNN but ResNet are skillful at it, the models have both performed at a very high level of accuracy. The main concept conveyed in this study is that the advanced deep learning structures have the chance to transform dermatological diagnostics by providing reliable and scalable solutions. The results suggest that the integration of AI and ML with healthcare will pave the way for better precision in diagnosing, reduced human error, and improved patient outcomes. In the future, it is possible to combine additional deep learning models, multi-modal data, and clinical trials so as to further validate and enhance the practicability of these systems in real-world medical settings.