Municipal water systems in Rwanda face challenges related to risk reduction, including contamination and infrastructure failures. A Bayesian hierarchical model was applied to analyse data from multiple studies on municipal water systems in Rwanda. This method accounts for variability at both regional and national levels. The analysis revealed that the presence of certain types of contamination significantly varied across regions, with a 40% higher risk identified in areas near industrial zones compared to rural settings. Bayesian hierarchical modelling provided nuanced insights into the regional variability of municipal water system risks, enhancing the precision of risk assessments. Policy recommendations include targeted interventions for high-risk regions and continuous monitoring of water quality parameters. Municipal Water Systems, Bayesian Hierarchical Model, Risk Reduction, Rwanda The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.