In this study, we present a workflow that constructs a feature space comprising physical properties and structural attributes for standardized benchmarking of ML models in predicting elastic properties, using a dataset of 10570 unique crystal structures from the Materials Project Database 40 , we evaluated the performance of eight different ML models on a subset of the feature space derived based on correlated feature importance using the MRMR algorithm . For the best-performing models in each case, we used SHAP (SHapley Additive exPlanations) derived feature importance to formulate a unified task-specific ranking of features. We next trained a GNN architecture, specifically the CGCNN 7 model, on a weighted feature space based on the overall ranking, which improved the performance of the CGCNN even with lower amounts of data for predicting the Bulk and Shear moduli.