Data-driven regression methods are becoming popular tools for predicting and designing novel materials. In glass, learning properties directly from glass composition is very common. However, these composition-based models are restricted to a particular set of compositions as an input for which they are trained. Herein, we develop physics-based descriptors that can predict the property for any given composition by transforming composition space into twelve universal descriptors space. To this extent, we trained ML models using XGBoost (Extreme Gradient Boosting) algorithm to learn the descriptor–property relationships for density, Young’s, shear, bulk moduli, thermal expansion coefficient, Vickers’ hardness, refractive index, glass transition temperature, liquidus temperature and abbe number having twelve universal descriptors as an input feature. Further, we interpreted these models using SHAP value analysis to understand contribution of descriptors in property value. Overall, these physics-based descriptors prove to be advanced, reliable, and global data-driven models to predict novel glasses' property.