In this study, I employed the Symmetric Gradient Domain Machine Learning (sGMDL) dataset, which comprises trajectories of various molecules in xyz format. Focusing on aspirin, I analyzed a trajectory consisting of 1500 frames, calculated using the CCSD method with Psi4 software, utilizing the cc-pVDZ basis set. This high-level electron correlation approach offers a more precise depiction of molecular behavior compared to Density Functional Theory (DFT) methods commonly used for determining molecular geometries. This trajectory served as our ground truth. I divided the data, using the initial 90% (1350 frames) for training and the remaining 10% (150 frames) for testing. My goal was to develop machine learning models capable of predicting these final 150 frames with the lowest possible Root Mean Square Deviation (RMSD).