The Utilization of Artificial Intelligence in Feed Formulation to Optimize Nutritional Efficiency and Livestock Productivity in Modern Farming Systems

Artificial intelligence (AI) is increasingly transforming livestock production by enabling data-driven approaches to feed formulation and nutritional management. This study aims to examine the utilization of AI in feed formulation to optimize nutritional efficiency and livestock productivity in modern farming systems. A library research method was employed by reviewing relevant books, peer-reviewed journal articles, scientific reports, and previous studies concerning AI, feed formulation, precision nutrition, nutritional efficiency, and livestock productivity. Data were analyzed using content analysis to identify patterns, relationships, technological applications, benefits, limitations, and research gaps. The findings indicate that AI-assisted feed formulation can improve nutrient matching by integrating animal characteristics, feed composition, environmental conditions, and production data. Machine learning and optimization algorithms can support nutritional prediction, feed efficiency improvement, productivity enhancement, cost optimization, and reduction of unnecessary nutrient losses. However, challenges remain regarding data quality, interoperability, model interpretability, technological costs, and field validation. AI therefore represents a promising decision-support technology for developing more precise, adaptive, productive, and sustainable livestock farming systems.