This study examined how climate-related flooding affected the performance of road infrastructure in Uganda, using flood-prone roads as a case study. A quantitative design was adopted, and a coded dataset of 110 floodprone road sections was analysed using descriptive statistics, Pearson correlations, multiple linear regression of flood-related closure days and binary logistic regression of severe flood damage, supported by diagnostic tests. The dataset was simulated for study purposes and was designed as a template to be replaced by real road agency and meteorological records. The study results indicated that roads were closed for an average of 9.4 days per year, and that more heavy-rain days (B = 0.92, p < 0.001), proximity to a wetland or river (B = 5.31, p < 0.001) and greater road age increased closure, while adequate culverts, paved surfaces and maintenance spending reduced it. Adequate culvert capacity (odds ratio = 0.11, p < 0.001) and paved surfaces (odds ratio = 0.12, p < 0.001) sharply reduced the odds of severe damage. Heteroscedasticity was detected and addressed with robust standard errors, which left the conclusions unchanged. The study concluded that climate hazards translated into road damage mainly through weaknesses in drainage and location, and it recommended climate-resilient design standards,culvert upgrading, risk-based maintenance and the systematic collection of flood data.