The purpose of grain storage management is to dynamically analyze the quality change of the reserved grains, adopt scientific and effective management methods to delay the speed of the quality deterioration, and reduce the loss rate during storage. At present, the supervision of the grain quality in the reserve mainly depends on the periodic measurements of the quality of the grains and the milled products. The data obtained by the above approach is accurate and reliable, but the workload is too large while the frequency is high. The obtained conclusions are also limited to the studied area and not applicable to be extended into other scenarios. Therefore, there is an urgent need of a general method that can quickly predict the quality of grains given different species, regions and storage periods based on historical data. In this study, we introduced Back-Propagation (BP) neural network algorithm and support vector machine algorithm into the quality prediction of the reserved grains. We used quality index, temperature and humidity data to build both an intertemporal prediction model and a synchronous prediction model. The results show that the BP neural network based on the storage characters from the first three periods can accurately predict the key storage characters intertemporally. The support vector machine can provide precise predictions of the key storage characters synchronously. The average predictive error for each of wheat, rice and corn is less than 15%, while the one for soybean is about 20%, all of which can meet the practical demands. In conclusion, the machine learning algorithms are helpful to improve the management effectiveness of grain storage.