Objective To predict the incidence trend of tuberculosis in Huangshan and to provide a basis for optimizing tuberculosis prevention and control strategies. Methods Tuberculosis incidence data from 2015 to 2024 in Huangshan were collected from the Tuberculosis Management Information System. Seasonal autoregressive integrated moving average (SARIMA) model, Holt-winters exponential smoothing model, Gray Prediction GM (1, 1) model, and Prophet model were constructed. The predicted incidence for 2024 from each model was validated against the actual reported incidence. Root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used as evaluation indicators to select the optimal model for predicting tuberculosis incidence from 2025 to 2026 in Huangshan. Results The registered tuberculosis incidence in Huangshan from 2015 to 2024 showed a fluctuating downward trend with distinct seasonality, peaked in summer, and reached a trough in winter. All four models demonstrated good fitting performance. Validation results revealed that the Prophet model achieved the best predictive performance (RMSE=5.03, MAE=4.17, MAPE=10.28%). All models predicted a continued decline in tuberculosis incidence in Huangshan for 2025-2026. The Prophet model predicted 462 and 432 registered tuberculosis cases in 2025 and 2026, respectively, corresponding to registered incidence rates of 35.08/100 000 and 32.80/100 000. Conclusion The Prophet model exhibits the best fitting performance and can be used to predict the trend of tuberculosis incidence in Huangshan.