Roads have always failed in patterns that physics could predict, but knowing which road will fail next—and how badly—has remained stubbornly difficult for those who maintain them. A research team has now built a two-stage machine learning framework that reads the visible signs of pavement deterioration and forecasts what comes next, achieving strong accuracy for cracks while honestly acknowledging the limits of sparse pothole data. The system's most telling contribution may not be its performance scores, but its willingness to distinguish between what it knows and what it merely glimpses—a dis