Public health is still threatened by food safety. Large, newly available data sets may be used by machine learning to enhance food supply safety and lessen the effects of food safety events. genomes of food borne pathogens and new data streams, such as transactional, text, and trading data, have observed new uses made possible by machine learning strategy, like the forecasting of antibiotic resistance, the attribution of sources to pathogens, risk assessment, and the identification of foodborne outbreaks. Within this In this post, we give a thorough introduction to machine learning with a focus on food safety, along with a summary of current advancements and uses. Despite the fact that many of these applications are still in their infancy, general and domain-specific machine learning pitfalls and challenges are starting to be identified and addressed. These developments are crucial for the potential use and future deployment of large data sets and the machine learning models that go along with them for food safety applications.