Forecasting the seasonality and trend of pulmonary tuberculosis in Jiangsu Province of China is crucial for resource allocation. Researchers compared two prediction models, ARIMA and BPNN, using data from 2005 to 2015. The BPNN model showed better performance in predicting tuberculosis rates. The study suggests that using statistical techniques tailored to local characteristics can improve the accuracy of mathematical modeling for predicting tuberculosis trends.