Artificial intelligence and internet of things enabled systems for smart veterinary disease detection

Livestock plays a pivotal role in global agriculture by providing diverse resources—from food to fertilizers—and contributing to economic empowerment. Among various livestock species, cattle hold a vital position in rural resource management and economic activity. However, the spread of infectious cattle diseases such as Lumpy Skin Disease (LSD), Foot and Mouth Disease (FMD), and Bovine Tuberculosis (BTB) gives rise to serious challenges, leading to substantial economic losses due to reduced milk productivity, miscarriages, increased treatment costs, and trade restrictions. Early detection of cattle diseases is essential to minimize losses and ensure effective disease management. This study presents a review of recent advancements in Internet of Things (IoT), machine learning (ML), and artificial intelligence (AI) technologies that are transforming veterinary services by enabling early disease detection. This paper presents a systematic literature review of 20 peer-reviewed studies published between 2017 and 2025, identified through major scientific databases including Scopus, SpringerLink, IEEE Xplore, ScienceDirect, PubMed, and Google Scholar. The study highlights AI-based techniques for the detection, classification, prediction, and monitoring of important cattle diseases such as LSD, bovine mastitis, FMD, bovine respiratory disease (BRD), and cardiovascular disorders, using image, sensor, and text-based data. Overall, this review highlights key trends, methodological limitations, and research gaps in the current literature, such as dataset scarcity, class imbalance, inconsistent evaluation metrics, limited external validation, and the relatively low adoption of Explainable AI (XAI) approaches. The findings provide valuable insights into current research practices and identify key areas for future work toward more reliable, transparent and practical AI- based cattle disease diagnosis.