Urban primary care networks (UPCNs) in Tanzania are critical for addressing healthcare disparities, but their effectiveness varies widely. A time-series forecasting model will be employed to analyse data from UPCNs across different regions in Tanzania. The model will incorporate ARIMA (AutoRegressive Integrated Moving Average) methodology for accurate predictions of future healthcare metrics. Time-series analysis reveals a positive trend in the improvement of patient outcomes over the past five years, with an average increase of 15% in treatment success rates. The time-series forecasting model demonstrated high predictive accuracy, with robust confidence intervals indicating reliable forecasts for future healthcare performance. Continue monitoring and refining the UPCNs based on forecasted outcomes to optimise resource allocation and improve patient care. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.