Meat is nutritious and highly susceptible to contamination by foodborne pathogens, spoilage bacteria and other harmful microorganisms, so that its food safety is of great concern. In recent years, machine learning methods have been extensively employed in the field of food safety. In this paper, we review the application of machine learning methods from two perspectives: detection and predictive modeling of harmful microorganisms in meat. This review also analyzes the limitations of machine learning methods at the present stage, and provides an outlook on their prospects in meat microbial safety.