State-of-the-art deep learning techniques for the detection of cattle infected by lumpy skin disease are presented in this chapter. This is a viral infection caused by poxvirus lumpy skin disease virus (LSDV), which is a dangerous threat to cattle health and the agricultural economy. So, early detection and diagnosis play a significant role for efficient disease prevention, management, and control. In this study, we utilized various deep learning models, such as CNN, MobileNet, ResNet50, EfficientNetB0, and VGG16, respectively, on a specific image dataset to classify the cases of lumpy skin disease into lumpy or normal skin. Every model was trained for 100 epochs followed by a strict assessment that made accuracy the focus of evaluation. In our experiments, we discovered that the VGG16 followed by the EfficientNetB0 model is superior to others in terms of metrics like accuracy and balanced F1-score in detecting the disease. VGG16 and EfficientNetB0 show 91.42% and 90.47% accuracy on the unseen dataset, respectively. These findings help in the development of automated systems for the early stage detection of the lumpy disease. These systems can play a crucial role in early detection and prevention of the mass spread of lumpy skin disease, hence protecting cattle health and boosting the agricultural sector.