Featurization plays a pivotal role in machine learning tasks, serving as a bridge between raw data and model inputs. In our study utilizing the PhononDB dataset, we leverage both crystal and elemental features to characterize materials comprehensively. The crystal features like bond lengths, lattice parameters, etc. are collected from the Materials Project database since the PhononDB dataset contains the material IDs as well. The elemental descriptors are quantified through mathematical expressions denoting their average, minimum, maximum, and difference values, respectively. Additionally, we have used graph-based features using CGCNN for the PhononDb dataset. However, the experimental JANAF dataset being devoid of the structural information is feature-engineered solely based on the compositions of the materials. Similar to the previous dataset, the same mathematical expressions have been employed here to characterize the elemental properties.