Food waste is a complex phenomenon. Food gets wasted for a range of different reasons, which are affected by a range of factors: to give a few examples at the household level, how people shop, what they buy, how those items are packaged, the time devoted to foodrelated activities, skills and capabilities relating to cooking and food management in the home, and attitudes to food safety (Quested et al., 2013). Given this complexity, there are many challenges and questions that need answering for those wishing to prevent food from being wasted or estimating the quantity of food being wasted. Ideally, empirical data would be obtained, but this is currently lacking, mainly due to the monetary and time cost of obtaining such data. Therefore, system-based simulation methods and modelling approaches are being developed using currently available data, as they can incorporate these complexities and allow these challenging questions to be answered. Numerous methods have been used to infer the amount of food loss, waste or surplus. This chapter introduces four of the most exciting contemporary food waste prediction and prevention approaches including discrete event simulation (DES), agent-based modelling, machine learning and Bayesian networks, and mass balance estimation (quantification of food waste using food availability, metabolism and calories consumed).