Seasonal flooding is the dominant cause of road network disruption in South Sudan, rendering large portions of the classified road network impassable for three to seven months annually and inflicting severe economic, humanitarian, and logistical losses. Despite the critical importance of quantifying flood-induced network disruption for infrastructure planning, maintenance programming, and humanitarian contingency management, no stochastic modelling framework calibrated to the South Sudan context has previously been published. This paper develops and applies a comprehensive stochastic model integrating three complementary probabilistic approaches: (i) a Gamma-distributed flood duration model fitted to 14 years of MODIS-derived inundation data for 12 road segments; (ii) a four-state continuous-time Markov chain (CTMC) model representing transitions between Accessible, Partially Flooded, Fully Impassable, and Under Rehabilitation road states, with seasonally varying transition rate matrices; and (iii) a Monte Carlo simulation framework generating 10,000 realisations of annual network-wide disruption to construct probability distributions of aggregate disruption metrics and Network Reliability Index (NRI). Model parameters are estimated by maximum likelihood estimation from remotely-sensed inundation records (2010–2023) and field condition survey data. The fitted Gamma flood duration model (shape α = 2.85, rate β = 18.4 days) provides a significantly better fit than exponential or log-normal alternatives (Anderson-Darling p = 0.312 vs. p = 0.038 and p = 0.071). The CTMC model predicts that the N-8 Juba–Bor corridor operates in the Accessible state for only 28% of days in August at its current condition, declining to a projected 19% by 2034 under the no-intervention scenario