During the process of food storage and circulation， there will be different degrees of quality deterioration. With the improvement of people's attention to food quality and safety， it is of great significance to carry out quality prediction research in the process of food storage and transportation for quality control. This paper reviews the research progress of machine learning in food storage quality prediction， including conventional quality prediction methods and limitations， and then focuses on the rapid development and wide application of integrated learning and artificial neural network algorithms， and prediction performance evaluation methods in recent years. Finally， it summarizes and looks forward to the future development trend of machine learning in the food field， and provides relevant references for the development of food science cross research.