Road infrastructure projects in Sub-Saharan Africa are characterised by persistent and substantial cost overruns (mean overrun ratio 1.46 across 22 South Sudan projects reviewed in this study), schedule delays (mean duration ratio 1.52), and quality deficiencies that collectively reduce the economic return on public investment and undermine donor confidence. Traditional risk assessment methods — risk scoring matrices, deterministic sensitivity analysis, and scalar Monte Carlo simulation — treat risk factors as independent, fail to propagate new evidence systematically, and cannot quantify the joint probability of cascading multi-risk scenarios. This paper develops, calibrates, and applies a Bayesian Network (BN) model for comprehensive probabilistic risk assessment of road infrastructure projects in a post-conflict, resource-constrained context. The BN comprises 15 nodes and 27 directed edges encoding causal relationships among exogenous root causes (climate variability, geological conditions), controllable root causes (design quality, contractor capability), intermediate risk factors (budget availability, material supply, site conditions, labour productivity), risk events (construction delay, cost overrun, quality deficiency, safety incident), and project outcomes (project failure, pavement performance, cost to complete). Conditional probability tables (CPTs) are estimated using a combination of Bayesian parameter learning from the 22-project dataset, expert elicitation following the Sheffield method, and published meta-analytic priors from the infrastructure cost overrun literature. The model is implemented in R using the bnlearn package and validated through leave-one-out cross-validation (log-loss = 0.35, Brier score = 0.13, AUROC = 0.89). Evidence propagation analy