Chilled meat is rich in nutrients but highly susceptible to contamination by spoilage microorganisms during slaughter, processing, storage and transportation. Microbial spoilage in foods has become a major concern for food safety. Predictive models for microbial growth in foods can judge the growth or survival of major spoilage microorganisms in foods, thereby allowing the evaluation and prediction of the quality and safety of meat. The artificial neural network (ANN), a complex nonlinear model, has the advantages of massively parallel processing, distributed storage, high adaptability, and fault tolerance. This paper reviews the main spoilage microorganisms in chilled meat and the application of traditional microbial prediction models and ANN models in meat to provide ideas for constructing microbial growth prediction models in foods.