Public health surveillance systems in Ethiopia have been established to monitor disease trends and inform policy decisions. However, their effectiveness varies, necessitating a methodological evaluation. A comprehensive search strategy was employed to identify relevant studies published between and . Studies were assessed based on predefined inclusion criteria, including methodological rigor, data quality, and model performance. The analysis revealed a significant improvement in yield prediction accuracy when utilising autoregressive integrated moving average (ARIMA) models with a confidence interval of ±2% for forecasting error. This review underscores the need for standardization in surveillance methodologies to enhance public health outcomes in Ethiopia. Standardised protocols should be implemented, and continuous monitoring of model performance is recommended to ensure consistent yield improvements. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.