The most common illnesses worldwide are those related to the skin. Despite being widespread, therapy is exceedingly challenging and necessitates substantial industry knowledge. Most skin conditions are brought on by bacterial, viral, allergic, or fungal infections. For the rapid and precise treatment of skin problems, medical technology based on photonics and the development of lasers is applied. However, there are few and very expensive medical devices available for this type of diagnostic. Deep learning algorithms are useful in these circumstances. Early skin disease detection is crucial for effective therapy. Convolution Neural Network (CNN), a deep learning approach, may aid in identifying the issue early on. We employed a combination of computer vision and deep learning to precisely diagnose illnesses. The frequency of frequent human supervision has decreased as a result of the application of Deep Learning algorithms. For the classification of skin diseases, a dataset of 5,633 photographs that are classified into 5 groups has been collected. They consist of urticaria (hives), acne, eczema, melanoma, and psoriasis. The model also lists the recommended treatments for the skin condition as well as the precautions that must be taken. 83% accuracy in classifying skin diseases is achieved using the CNN algorithm.