Developing novel glasses with new, improved properties and functionalities is key to address some of the Grand Challenges facing our society. Although machine learning offers a unique opportunity to accelerate the discovery of novel glasses with exotic functionalities, it faces several challenges. In particular, the use of machine learning requires as a prerequisite the existence of data that are (i) available, (ii) complete, (iii) consistent, (iv) accurate, and (v) numerous. For instance, although some glass property databases are available, inconsistencies between data generated by different groups render challenging any meaningful application of machine learning approaches. Here, we present a new machine learning framework that simultaneously leverages experimental and simulation-based (synthetic) data by means of Multi-Fidelity Gaussian Process Regression (GPR). We show that our hybrid model systematically outperforms models relying solely relying on experimental data.