The skin is the largest and fastest progressive part in the body. The skin’s immune cells interact with keratinocytes in a number of ways to trigger a mass of dermatitis. Both living immune cells and skin-penetrating cells can coordinate with keratinocytes to promote pathogenesis of the disease. The activated cells make chemokines that attach to the immune cells in the skin. According to the latest data released by WHO in 2018, the number of deaths due to skin cancer in Bangladesh has reached about 0.04%. The mortality rate is 0.27% per 100,000 populations by age. The author has proposed an exploration using Transfer Learning on six types of skin disease as- Peeling, Acne, Eczema, Heat-rash, Melanoma, and Cold sore. The classification of these skin conditions was done using a Convolutional Neural Network. For the comparison with CNN, four state-of-art Transfer Learning models have been applied such as NASNetLarge, InceptResNetV2, EfficientNetB1 and DenseNet169, in which NASNetLarge (Accuracy 90% & Validation 80%) has given the highest accuracy. And our state of model NASNetLarge can perfectly recognise the disease types than the others. Through images processing, skin experts can initial treatment of skin diseases through observation of images of affected areas. As a result, the type of disease can be ensured and consequently can be ensured to reduce the complexity and disorders of skin diseases.