Goat farming can be said to be one of the branches of agricultural activity that is of great importance to the economy of a nation like India. The raising of livestock, especially in the rural parts, pays about 4% of the GDP of the country in the form of employment and food security. However, a shortage of veterinarians occurs in dry areas, which creates a barrier for farmers to receive veterinary care on time. If animals are not properly supervised, the risk of goats developing complications or dying from untreated infections is very high. Apart from herding goats, most farmers don’t bother to look for signs of illness in their animals; such signs include fever and low feed intake, which means by the time some of them recognize that their goats are sick, it is often too late and they incur huge losses. Some small-scale farmers, on the other hand, do not vaccinate their animals enough due to the costs of vaccination, without which the diseases become rampant. This study presents an application that enables the identification of goat diseases and recommends treatment through an image-based prediction. For effective detection, the model is trained using YOLO after employing a specialized image feature extraction technique, all the while bettering test image accuracy with ResNet-50. The accuracy of the model is 82.67%. The application can offer simple medical prescriptions and instructions for disease control. Considering low-powered devices, the approach does not connect to the internet, thus making it possible to provide near real-time predictions assisting farmers in far-flung areas to take good care of goats and limit losses.