Public health surveillance systems in Tanzania are crucial for monitoring diseases and managing resources effectively. A systematic literature review was conducted to assess the methodologies used in public health surveillance systems across various studies from onwards. Time-series forecasting models were applied to analyse cost-effectiveness data. Time-series forecasts suggested that incorporating real-time data improved cost-effectiveness by reducing healthcare costs and enhancing resource allocation efficiency, with a forecast accuracy of ±10% in the short term. The review identified several methodological gaps but highlighted the potential for improving surveillance systems through advanced forecasting techniques. Future studies should consider integrating real-time data into public health surveillance frameworks to optimise resource utilization and cost-effectiveness. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.