This research develops a deep learning-based system to detect skin illnesses to enhance early diagnosis and intervention. The study uses a deep convolutional neural network called ResNet50, which provides 83.61% accuracy in detecting the disease and generalization using residual connections and pre-trained weights. To improve model performance, the technique requires gathering data from a variety of sources and conducting an exhaustive preprocessing stage which involves augmentation and standardization. This project also uses image processing and machine learning to create a web-based application for the early diagnosis and treatment of skin conditions. With its easy-to-use interface, the system allows users to upload skin pictures for automated diagnosis which offers a dependable and easily accessible tool for dermatological early detection and intervention. The solution is accessible, dependable, and efficient in enhancing healthcare results.