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Sub-Saharan Africa faces a substantial burden from both communicable and non-communicable diseases, yet gaps in health data tracking hinder understanding of health impacts and treatment affordability. This study examined disease patterns in the Rwenzori region (2013–2022) using Disability-Adjusted Life Years (DALYs) and assessed treatment costs and affordability perceptions through ordinary least squares (OLS) and Bayesian regression. DHIS2 records provided annual disease data, while survey data from 284 households informed analyses of treatment expenses and perceived affordability. Findings indicate that malaria, coughs, and intestinal diseases remain prevalent, with hypertension and cancer increasing in incidence. DALY analysis identified hypertension as the leading contributor to disease burden, followed by pregnancy-related hemorrhage and malaria. OLS regression showed that treatment costs were highest in private facilities, in households managing multiple conditions within a given period, and especially among elderly patients with persistent NCDs, highlighting a strong age–disease interaction (β = 173,013, p = 0.014). Bayesian regression indicated that caring for individuals over 60 (Median = 1.71, 95% CI: [0.54, 2.72]) and households with seven or more members (Median = 0.78, 95% CI: [0.25, 1.29]) exerted the greatest influence on perceived affordability. While distance to healthcare facilities did not directly affect treatment costs, it significantly shaped affordability perceptions. The study underscores the need for policies that improve healthcare affordability for vulnerable populations, including the elderly and large households. Strengthening health information systems and prioritizing preventive care for NCDs are essential. Bayesian approaches provide valuable insights to support evidence-based healthcare planning in resource-limited settings.