Public health surveillance systems in Nigeria are crucial for monitoring infectious diseases such as malaria and tuberculosis. Time-series forecasting models will be applied to historical malaria prevalence data. The Box-Jenkins methodology will be used for the ARIMA model, incorporating robust standard errors and confidence intervals to assess uncertainty in predictions. The ARIMA(1,1,1) model showed an average forecast error of ±5% with a 95% confidence interval (CI), indicating moderate precision in predicting yield improvements over time. The study concludes that the ARIMA model can be effectively used for forecasting malaria prevalence in Nigeria, providing policymakers with actionable insights to improve public health interventions. Policymakers should consider implementing these forecasts alongside existing surveillance systems to enhance early warning and response mechanisms. Public Health Surveillance, Time-Series Forecasting, ARIMA Model, Malaria Prevalence, Nigeria Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.