Public health surveillance systems in Nigeria are crucial for monitoring disease trends and controlling outbreaks. However, their effectiveness varies, necessitating a comprehensive evaluation. A systematic review of existing surveillance data was conducted to select appropriate studies. Time-series forecasting models, including ARIMA (AutoRegressive Integrated Moving Average), were applied for analysis. The ARIMA model indicated a mean forecast error rate of ±5% in predicting disease incidence trends, with some variability across different regions. Time-series forecasting models provide valuable insights into the performance and potential improvements of surveillance systems in Nigeria. Enhanced training for surveillance staff and integration of new technologies are recommended to improve system yield. Public Health Surveillance, Time-Series Forecasting, ARIMA Model, Nigeria Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.