Rebalancing vacant vehicles is one of the most critical strategies in ride-hailing operations. An effective rebalancing strategy can significantly reduce empty miles traveled and reduce customer wait times by better matching supply and demand. While the supply (vehicles) is usually known to the system, future passenger demand is uncertain. There are two ways to handle uncertainty. First, the point-prediction-driven optimization framework involves predicting the future demand and then producing rebalancing decisions based on the predicted demand. Second, the data-driven optimization approaches directly prescribe rebalancing decisions from data. In this study, a predictive prescription framework is introduced to this problem, where the benefits of predictive and data-driven optimization models are combined. Based on a state-of-the-art vehicle rebalancing model, the matching-integrated vehicle rebalancing (MIVR) model, predictive prescriptions are introduced to handle demand uncertainty. Model performances are evaluated using real-world simulations with New York City (NYC) ride-hailing data under four demand scenarios. When demand can be accurately predicted, a point-prediction-driven optimization framework should be adapted. The proposed predictive prescription models achieve shorter customer wait times over the point-prediction-driven optimization models when future demand predictions are not so accurate, and achieve a competitive performance with respect to the cutting-edge robust optimization models.