We propose a robust framework for coflow scheduling that operates in online settings when only predictions of flow sizes are available. Our approach generalizes classical coflow scheduling policies such as Sincronia and MUWP-based methods and extends them with prediction-aware decision rules. We establish formal performance guarantees showing that the resulting schedules remain competitive with the optimal clairvoyant offline solution, and we further characterize how the accuracy of the predictions influences the overall cost.