Systematic road condition assessment is a prerequisite for rational maintenance programming and rehabilitation investment decisions, yet conventional field survey methods are prohibitively expensive, logistically constrained, and inaccessible in large areas of South Sudan due to insecurity and seasonal flooding. This paper presents a machine learning (ML) framework for automated road condition assessment using freely available multi-spectral satellite imagery, applied to the classified road network of South Sudan. Six ML models are evaluated — Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Convolutional Neural Network (ResNet-50 architecture), a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN+LSTM) model for temporal feature fusion, and Logistic Regression as a baseline. Input features comprise 24 spectral and textural variables derived from Sentinel-2 Level-2A imagery (10–20 m resolution), Planet NICFI high-resolution basemaps (4.77 m resolution), and derived indices including the Normalised Difference Built-up Index (NDBI), Bare Soil Index (BSI), Modified Normalised Difference Water Index (MNDWI), and Gray-Level Co-occurrence Matrix (GLCM) texture features. Ground truth Road Condition Index (RCI) labels were derived from 1,660 road segments surveyed by the Ministry of Roads and Bridges using standard visual and measurement protocols during February–April 2023. The CNN+LSTM model achieves the highest performance with an Overall Accuracy of 93.5%, Cohen's Kappa of 0.899, and macro-averaged F1 score of 0.922, outperforming XGBoost (89.2%, 0.843, 0.881) and Random Forest (87.4%, 0.821, 0.863). A predicted RCI map for the full classified network (approximately 8,400 km) is generated, revealing that 64% of the network