Air pollution has a variety of effects on human skin. In heavily populated areas, skin problems are very frequent. These diseases have a severe effect on people's life because they create a significant demand for disease diagnosis. The suggested study on a skin disease detection system uses image processing to get an accurate diagnosis. By studying the input image, the process detailed here seeks to discover skin illness. Filtering the input to eliminate noise, converting the image to a grayscale image, and image segmentation are all part of the approach. Feature extraction is used to reduce the quantity of data that the classifier has to process. The SVM (Support Vector Machine) is then used in the image classification to identify the skin disease. The greater use of technology has resulted in a more efficient and accurate manner of diagnosing the disease, allowing it to be treated more quickly. Skin illnesses such as rosacea, melanoma, psoriasis, and acne can be detected with an accuracy of 89 percent using the proposed method.