Urban primary care networks (UPCNs) in Tanzania aim to improve health outcomes through efficient service delivery. However, methodological challenges exist in assessing clinical outcomes within these systems. A systematic review was conducted to identify relevant studies on UPCN performance metrics, including patient satisfaction, health equity, and cost-effectiveness. The analysis employed Bayesian hierarchical modelling to account for variability across sites and time. Bayesian hierarchical models showed significant improvement in clinical outcomes when applied consistently across all sites, with an average reduction of 20% in patient wait times attributed to centralized coordination. The use of Bayesian hierarchical models provided a robust framework for evaluating the performance of urban primary care networks. Future research should focus on scalability and cost-effectiveness. Future studies should consider expanding model application to rural areas and incorporate real-time data integration capabilities. Urban Primary Care Networks, Clinical Outcome Assessment, Bayesian Hierarchical Models, Health Equity, 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.