| Sumario: | Early detection and prediction of diseases in livestock are essential to ensure animal welfare, improve productivity, and reduce economic losses in the livestock sector. In this context, Machine Learning (ML), a key advancement within artificial intelligence, emerges as a transformative tool for managing animal health. This technology enables the development of complex algorithms capable of analyzing large volumes of clinical and environmental data, identifying early warning patterns in symptoms and behaviors associated with diseases. Through predictive models, ML assesses risk factors and estimates the likelihood of disease occurrence, significantly enhancing diagnostic accuracy and treatment effectiveness. This article provides a comprehensive review of ML's use in livestock production, covering cutting-edge applications, models, and techniques for disease detection and management in livestock. It also highlights the ethical and privacy challenges that accompany the implementation of these technologies, aiming to pave the way for more efficient and responsible livestock management
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