In this study, we focused on 20 metabolites and employed a DFT based workflow to compute their Collisional Cross Section values. Our workflow has demonstrated good accuracy in CCS prediction, with an error rate of less than 3% compared to experiment (experimental error is ~3%). Our established workflow encompasses the following steps to predict accurate CCS values: First, the conformations of each metabolite were generated using tools contained within RDKit. The conformation of the molecule is initially generated using the ETKDG algorithm, an embedded method within RDKit, employing default settings, followed by structural optimization of the generated conformations using the MMFF94 force field. A maximum number of generated conformers is set to 1000 for the small molecules systems. Each generated conformer was then geometry optimized using the ANI QM-ML model. The optimized structures were subsequently clustered using our in-house automated clustering code AutoGraph, enabling the identification of chemically distinct conformations as centroids of the identified clusters. Geometry optimization and Mulliken atomic charge calculations were performed on representative conformations of each identified cluster using B3LYP/6-31+G(d,p) and B3LYP/6-311++G(d,p) level of theory, respectively employing a GPU enabled, in-house developed QM engine called QUICK. The CCS values were computed using the trajectory method (TM) as implemented in the HPCCS code developed by Zanotto et al. The inclusion of an unsupervised clustering method in our workflow reduces the potential for human bias and error in cluster selection, while the QM-ML model and clustering technique contribute to its computational efficiency.