This study addresses the challenges of forecasting logistics demand in Hubei Province by utilizing the GM (1,1) model from grey system theory. The model is adept at managing the issues of small data samples and incomplete information, a common hurdle in logistics analysis. It offers a robust method for predicting future logistics needs by constructing grey differential equations from time series data. The application of this model to Hubei’s logistics data allows for a detailed analysis that accounts for dynamic changes and reduces the impact of random noise. The result is a set of forecasts that are more reliable and reflective of real-world conditions. These insights are crucial for optimizing logistics operations, improving efficiency, and allocating resources effectively in Hubei and can serve as a reference for other regions facing similar forecasting difficulties.