Growing complexity of global food supply chains has escalated the risk of foodborne illness, calling for ever more proactive measures for food safety. Predictive modeling has emerged as a breakthrough approach by enabling early detection of potentially contamination events and guiding effective risk-mitigating interventions. This study proposes a predictive multi-model approach integrating statistical, machine learning, and deep learning methods to strengthen the identification and classification of food safety risks. Founded on microbiological, environmental, and operational data sets, the framework will be designed to be scalable, modular, and adaptable to various food commodities and geographical regions. A case example of Listeria monocytogenes in ready-to-eat meat products demonstrates the increased performance of the ensemble model in accuracy and recall. The findings emphasize the value of predictive analytics to enhance regulatory programs and public health outcomes if applied in a transparent and clearly defined manner.