Template-Type: ReDIF-Article 1.0 Author-Name:Muhammad Mohsin Raza, Rabia Tehseen1, Uzma Omer, Muhammad Qasim, Usman Aamer, Ramsha Saeed,Muhammad Farrukh Khan Author-Email:rabia.tehseen@ucp.edu.pk Author-Workplace-Name:Department of Computer Science, University of Central Punjab, Lahore, Pakistan, Department of Computer Science, University of Management & Technology, Lahore, Pakistan, Department of Computer Science, University of Education, Lahore, Pakistan, Department of Computing, NASTP Institute of Information Technology, Lahore, Pakistan Title:Role of Machine Learning in Livestock Health Monitoring System: A Systematic Literature Review Abstract:Machine Learning (ML) can significantly enhance livestock management in various ways by providing real-time insights into animal health, behavior, and well-being. Livestock production, monitoring, and management can be revolutionized by using ML techniques. This study presents a comprehensive review of the literature regarding IoT devices used for monitoring cattle health, key characteristics of these devices, wearable technology used, sensors, and ML algorithms. In order to complete the review, a thorough examination and synthesis of the research articles published in reputable research venues between 2018 and 2023 are conducted. The findings revealed that pressure and pulse-rate sensors are the most often utilized types for recording the health status of animals experiencing health issues. Keywords:Machine Learning, IoT, Livestock Health System, Precision Livestock, Livestock Monitoring, Animal Welfare, Precision Farming, Livestock diseases Journal:International Journal of Innovations in Science and Technology Pages:986-1005 Volume:7 Issue:2 Year:2025 Month:May File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1403/1917 File-Format: Application/pdf File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1403 File-Format: text/html Handle: RePEc:abq:IJIST:v:7:y:2025:i:2:p:986-1005