This study models HIV prevalence among individuals aged 15-49 in Uganda, by utilizing historical data from 1990 to 2022 using an autoregressive integrated moving average (ARIMA) approach. Time-series data from the World Bank is employed, with HIV prevalence (% of population aged 15-49) as the dependent variable while autoregressive (AR) and moving average (MA) components are the independent variables. Parameter estimation, conducted using generalized least squares (GLS), revealing a negative and statistically significant AR(1) coefficient (-0.705706), implying a strong inverse relationship between the previous year’s HIV prevalence and the current year’s prevalence accounting for approximately 71%. Estimated ARIMA (1, 2, 5) model is covariance stationary and invertible, confirming its robustness in forecasting HIV prevalence trends. Projections indicate a gradual stabilization between 5.2% in 2023 and 5.1% by 2042, signaling diminishing returns in reducing HIV prevalence rates. We recommend implementing targeted interventions including effective HIV prevention and treatment programs and supporting long-term strategies to reduce HIV prevalence in the country.