Skin diseases are a common eventuality amongst humans. Diseases such as Melanoma has dangerous impacts and have high potential to infect other parts of the body. Due to the differences in their skin texture, presence of skin hair, and color, diagnosis is very difficult. To improve accuracy of diagnosis for various skin conditions, techniques like machine learning must be developed. The Medical Decision Support System will assist in accurately detecting the various kinds of skin diseases using a range of images that have been trained on datasets. Because manual diagnosis can be labor-intensive and time-consuming, the system uses Convolutional Neural Networks, with a variety of models, and feeds the most accurate model into the Convolution Block Attention Model (CBAM). This technique's CBAM module makes the system operate to provide high accuracy with effective computing. By deploying a web application, the technology also makes it possible for physicians all over the world to utilize for analysis.