The search for new materials with exceptional elastic properties has mostly relied on high-fidelity albeit computationally costly physics-driven models such as density functional theory (DFT) or molecular dynamics (MD) based structure search techniques followed by experiments in some cases for validating the predictions . ML models have been greatly accelerating this discovery process by predicting materials with target properties (e.g., elastic) by providing a fast and accurate alternative to the physics-based approaches. However, the typical application of ML models in predicting mechanical properties has been limited to only either a selected class of materials (e.g., alloys, glasses, etc.) or a few sets of ML tools. They also do not inform on the reason behind the selection of a particular ML tool across many available options such as kernel ridge regression (KRR) , Decision tree , Gaussian process regression (GPR) , Gradient Boosting (GB) , Random Forest (RF) , and Support Vector Regression (SVR) etc. Additionally, various datasets with variability in computational techniques used to compute the targeted properties have been reported in previous studies . The choice of feature selection thus varies across different studies, with differing emphasis on feature importance. This results in serious nonuniformity and interpretability issues.