This study models tuberculosis (TB) case detection rates in Uganda using historical data from 2000 to 2022 and applies autoregressive integrated moving average (ARIMA) approach for time-series analysis. Data sourced from the World Bank is utilized, with TB case detection rate (%, all forms) as the dependent variable, while autoregressive (AR) and moving average (MA) components serve as independent variables. Parameter estimation, performed using conditional least squares (CLS), reveals a negative and statistically significant MA(1) coefficient (-0.896946), suggesting that approximately 90% of the current TB case detection rate is influenced by shocks or errors from the previous period. The estimated ARIMA (1, 2, 1) model is covariance stationary and invertible, confirming its robustness for forecasting trends in TB case detection rates. Projections from 2023 to 2032 suggest a gradual improvement, with the forecast indicating a steady upward trend from 92.3% in 2023 to 95.1% by 2032, although it falls short of 100% detection rate target. We recommend strengthening surveillance systems, enhancing policy interventions, and ensuring continuous investment in TB detection efforts to sustain this positive trajectory.