This study investigates unemployment trap in Uganda by analyzing historical trends and forecasting unemployment dynamics from 1991 to 2022 using a quantitative approach based on autoregressive integrated moving average (ARIMA) modelling. Time-series data from the World Bank is employed, with unemployment, total (% of total labor force) serving as the dependent variable, while autoregressive (AR) and moving average (MA) components act as independent variables. Parameter estimation is conducted using maximum likelihood estimation (MLE), revealing that MA(8) coefficient (0.6806) is positive and statistically significant, indicating a strong influence of past unemployment shocks on current levels. The estimated ARIMA (4, 1, 8) model is found to be covariance stationary and invertible, confirming its robustness for forecasting unemployment trends. Projections from the model suggest a persistent unemployment trap, with unemployment rates expected to fluctuate between 3.04% in 2023 and approximately 2.96% by 2042. These findings highlight a structural unemployment challenge in Uganda, with rates stabilizing at approximately 3%, signaling limited progress in addressing unemployment over time. The study recommends targeted policy interventions, including skills development programs, entrepreneurship promotion, and labor market reforms to stimulate job creation and reduce structural unemployment.