Objective To evaluate feasibility of autoregressive integrated moving average (ARIMA) model in predicting the incidence of tuberculosis (TB). Methods Using statsmodels module-based Python language, incidence of TB in Tianjin City from January 2004 to December 2015 was as training set, the optimal seasonal ARIMA (SARIMA) model was established, data from January to December 2016 were used to evaluate the efficacy of SARIMA model, and monthly incidence of TB in Tianjin City from January 2017 to December 2019 was predicted. Results Epidemiological results showed that monthly incidence of TB in Tianjin showed a overall downward trend from January 2004 to December 2015. There was a of peak disease incidence in 2005-2008, which dropped sharply after 2009 and then stabilized. From January 2017 to December 2019, monthly incidence of TB in Tianjin City declined steadily compared with previous years. The established optimal model was SARIMA(1,1,1)×(3,1,1)₁₂, residual BOX-Ljung statistic of the model was P=0.493, which indicated that the residual was a white noise sequence and the model fitted well. The actual value of predicted results was within 95% confidence interval of predicted value. Conclusion SARIMA (1,1,1)×(3,1,1)₁₂ model can accurately predict the monthly incidence of tuberculosis in Tianjin City.