Global warming, accompanied by the depletion of natural resources and diminishing water sources, has spurred the widespread establishment of camel farms, leading to increased camel interaction. This heightened proximity, compounded by confinement within restricted housing conditions, has been linked to changes in camel behaviour and immune function, escalating the risk of disease transmission. Skin infections, highly contagious in nature, pose significant economic burdens on camel husbandry. In animal research, advanced deep-learning techniques play a pivotal role, particularly in the continuous monitoring of camel health and welfare. By harnessing artificial intelligence (AI), early identification of infected camels becomes feasible, facilitating prompt alerts to veterinarians and curbing the spread of infections. Skin infections can be effectively categorized into bacterial dermatosis, fungal infections, and hypersensitivity allergic dermatosis using four CNN model architectures. At the Fujairah Research Centre (FRC), machine learning methodologies have successfully detected and diagnosed several camel diseases, yielding highly promising outcomes. These technologies exhibit considerable potential in discerning between healthy and infected skin patterns and properties in images, including lesion size, shape, and color. Additionally, they can classify the severity of skin infections based on the affected body area and the number of infection spots. This technology expedites illness detection and enables early isolation to contain the spread of contagious skin diseases.