Objective To investigate the feasibility of the autoregressive integrated moving average model (ARIMA) in analyzing foodborne diseases in Hubei Province and predicting the future trend of foodborne diseases in Hubei Province. Methods Based on the number of reported cases of foodborne diseases in Hubei Province for eight consecutive years (2014-2021), an ARIMA model was constructed using Python software to fit the data. The model was validated and parameters were optimized with data from January 2022 to December 2022. The optimal fitting model was used to predict the incidence and trends of foodborne diseases in 2023. Results The incidence of foodborne diseases in Hubei Province showed seasonal periodicity, and the peak of epidemic was usually in July every year. SARIMA (0,1,0) (2,2,1)₁₂ was determined as the best fitting model. The model extrapolation effect was verified with 2022 data, and MAPE was 23.90 %, indicating that the model extrapolation effect was effective. Conclusion The SARIMA model is effective for short-term prediction of foodborne disease incidence trends in Hubei Province, and can provide a scientific basis for the formulation of foodborne disease prevention and control policies in the coming year.