The developing viral illness of cattle known as lumpy skin disease (LSD) has terrible economic consequences. The virus responsible for lumpy skin disease is a member of the Poxviridae family and the Capripoxvirus genus. It is an economically significant transboundary disease that affects cattle, water buffalo, and camels. The symptoms of the disease include the development of lumps or nodules on the skin of the animal. This illness is widespread in Africa and the Middle East and has recently appeared in Asia. This article discusses the Lumpy skin disease outbreak and detection among cattle using Mobilenetv2 and Deep learning techniques. The lumpy skin disease dataset is utilized for this experiment and is balanced using the oversampling technique. MobileNetv2, a pre-trained neural network extracts the features from the images for image processing. Later deep learning model with the combination of a two-dimensional convolution neural network, max pooling, flatten and dense layer is utilized for classification purposes. The proposed model outperforms in terms of lumpy skin disease detection; the performance is compared using confusion metrics parameters with a classification accuracy of 99.88%.