To enhance the resilience of food systems to food safety risks, it is vitally important for national authorities and international organizations to be able to identify early signals of emerging food safety risks and to provide early warning in a timely manner. This review provides an overview of existing and experimental applications of artificial intelligence (AI), big data, and internet of things tools and methods as part of early warning and emerging risk identification in the food safety domain. There is an ongoing rapid development of systems fed by numerous, real-time, and diverse data with the aim of early warning and identification of emerging food safety risks. The suitability of big data and AI to support such systems is illustrated by two cases in which climate change drives the emergence of risks, namely, harmful algal blooms affecting seafood and fungal growth and mycotoxin formation in crops. Automation and machine learning are crucial for the development of future real-time food safety risk early warning systems. Although these developments and tools increase the feasibility and effectiveness of prospective early warning and emerging risk identification, their implementation may prove challenging, particularly for low- and middle-income countries due to low connectivity and data availability. It is advocated to overcome these challenges by improving the capability and capacity of national authorities, as well as by enhancing their collaboration with the private sector and international organizations.