UTTAR PRADESH JOURNAL OF ZOOLOGY

The global dairy industry suffers persistent productivity losses from poorly detected infectious, metabolic, and locomotion-related diseases in cattle. Early, accurate diagnosis matters enormously—for animal welfare, farm economics, and antimicrobial stewardship alike. Machine learning (ML) and artificial intelligence (AI) have, over the past two decades, become central tools within precision livestock farming, promising automated, continuous, and non-invasive health surveillance at herd scale. This review synthesises peer-reviewed literature from 2006 to February 2026, covering the principal ML algorithms—artificial neural networks, support vector machines, random forests, gradient boosting, and deep learning architectures—as applied to the most economically important dairy cattle diseases: mastitis, lameness, metabolic disorders, bovine respiratory disease, and deteriorating body condition. It evaluates the sensor modalities used, including accelerometers, pressure mats, infrared thermography, acoustic sensors, computer vision systems, and milk composition analysers, and assesses data integration strategies that have been proposed. Despite encouraging accuracy metrics under controlled conditions, inconsistent validation standards, limited dataset sizes, poor generalisation across farm environments, and a thin body of real-world implementation evidence remain major obstacles to clinical adoption. Ethical questions around data ownership, farmer digital literacy, and algorithmic accountability are also addressed. Progress towards genuine clinical utility will require standardised benchmarking, larger multi-farm prospective datasets, and transparent, explainable models aligned with emerging regulatory requirements and farmer needs.