Precision animal agriculture is undergoing a revolution thanks to the quick development of big data analytics and the incorporation of machine learning (ML) and data mining tools. The utilization of these technologies is essential for strengthening predictive analysis, optimizing resource use, and raising overall production as the agricultural sector shifts to more data-driven methods. The use of IoT-based data collecting, machine learning algorithms, and data mining to forecast animal health, behavior, and production outcomes is the main emphasis of this symposium paper, which examines the revolutionary potential of big data analytics in precision animal agriculture. The study illustrates how predictive models may be created to foresee illness outbreaks, optimize feeding schedules, and improve overall animal wellbeing by utilizing massive datasets from sensors and Internet of Things devices. To demonstrate the usefulness of these methods, a case study on ML models for cattle health prediction is given. The study also discusses the difficulties and constraints associated with incorporating Big Data analytics into conventional farming systems, including the need for farmer education and training, high implementation costs, and data privacy concerns. The purpose of this conversation is to draw attention to how important advanced analytics will be in determining the direction of precision animal husbandry and advancing sustainable agricultural methods in the future.