Skin diseases are more prevalent than other diseases. Skin diseases can be challenging to diagnose. They could spread fast to other parts of the body if left untreated, leading to serious infection. Skin diseases refer to a range of conditions that affect the skin These diseases can affect people of every age group and can range from mild, temporary conditions to chronic, life-long conditions. Skin diseases can affect various parts of the skin and can manifest in various forms such as rashes, itching, redness, scaling, blisters, or discoloration. Some skin diseases can be caused by genetic factors, infections, allergies, autoimmune disorders, environmental factors, or lifestyle habits. This paper proposes an ensemble model for various skin diseases detection using convolutional neural networks (CNNs). The proposed model combines three different CNN architectures, namely, Sequential CNN, ResNet-101v2, and DenseNet-121, to improve the accuracy and robustness of skin diseases classification. The proposed model takes skin lesion images as input and produces an average distribution over various skin diseases as output. The model is trained and evaluated on a large-scale skin disease dataset with 10 different skin diseases, achieving state-of-the-art performance in terms of accuracy. The results show that the proposed ensemble model outperforms individual CNN architectures and other state-of-the-art skin disease detection models. The main objective of the project is to create an application that helps in identifying the kind of skin disease easily.