Public health surveillance systems (PHSSs) in Ethiopia play a critical role in monitoring air quality, which is vital for public health interventions and policy-making. However, their effectiveness and reliability are often under scrutiny. The analysis employs meta-analysis techniques to aggregate data from various studies conducted across Ethiopia. Time-series forecasting models are utilised to predict future trends based on historical air quality data and other relevant environmental indicators. A specific time-series model revealed that the PHSSs in Ethiopia demonstrated a moderate level of reliability, with an estimated accuracy rate of around 75% over the study period (-). The findings suggest improvements are necessary to enhance the predictive capabilities and timeliness of these systems. Enhanced data collection methods and regular model calibration should be implemented to improve system performance. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.