Present paper reviews about the origin, subfields, mandates and application of artificial intelligence in animal disease diagnosis. Artificial intelligence (AI) is intelligence manifested by machines and has developed into subfields; Machine and Deep learning. Machine learning (ML) provides application of algorithms for identification of patterns of data and creates a model to make future predictions. Commonly used algorithms are linear regression, random forest, decision tree, K-nearest and support vector machines. In deep learning, algorithms are classified into categories; Convolutional neural network, Restricted Boltzmann Machines, Auto encoder and Sparse Coding. The Convolutional Neural Networks (CNN) is one of the most notable approaches, doesn’t require human supervision and automatically detects the significant features. Some of the commendable CNN architectures presented at ILSVRC (ImageNet Large Scale Visual Recognition Challenge (ILSVRC); AlexNet, ZFNet, VGG-16, GoogLeNet etc. Regarding use of AI technique in veterinary sciences, this paper reviewed some of the documented data of its application in disease prediction and diagnosis; The National Animal Disease Referral Expert System (NADRES) of ICAR-NIVEDI, detection of left atrial enlargement on canine thoracic radiology (Li et al., 2021), Predicting survivability and need for surgery in Horses with Colic (Fraiwan et al., 2020), detection of sub clinical mastitis in cows with the help of machine learning by Ebrahimie et al. (2018), CNN (GoogleNet) in discriminating between meningiomas and gliomas in canines MRI’s (Banzato et al., 2018) and using a xenograft platform and machine learning in development of exosomal gene to detect residual disease in dogs with osteosarcoma (Makielski et al., 2021).