Precision feeding not only improves nutrient absorption efficiency but also minimizes negative environmental impacts. The precision feeding method uses an automated feeding system connected to a computer system to leverage data collected from individual pigs. These data are processed and analyzed based on mathematical models to provide predictions and warnings to farmers or to develop feeding formulas tailored to the needs of each animal at different times in the production cycle. Although extensive research has focused on precision feeding systems to improve animal health, challenges related to cost, time, and equipment installation still exist. Additionally, with climate change, environmental factors, and feed quality, the diversity and complexity of pathogens sometimes lead to animals being infected with multiple pathogens simultaneously, making disease diagnosis and timely control challenging. The use of simulation is an effective method to study diseases, as it is a safe and cost-effective approach to forecasting, evaluating disease control strategies, and developing preventive measures. This paper focuses on key issues: building a cloud-based simulation program for the precision feeding system using GAMA and integrating an outbreak model with scenarios involving multiple pathogens. The results, based on data collected from precision feeding of pig herds in different simulation scenarios, are compared and analyzed with real-world experimental results to assess accuracy.