Our results showed that out of the 20 metabolites, the QM method achieved accurate CCS predictions for 13 metabolites, resulting in a success rate of 65%. Among the conformation generation engines, Auto3D demonstrated accurate CCS predictions for 8 molecules, yielding a success rate of 40%. CREST performed well, achieving accurate CCS predictions for 9 metabolites with a success rate of 50%. However, Balloon and ETKDG exhibited lower accuracy, correctly predicting CCS values for only 5 and 6 metabolites, respectively, with success rates of 25% and 30%. The average errors in CCS predictions for the QM, Auto3D, CREST, Balloon, and ETKDG generated conformational ensembles were found to be 2.5%, 4.9%, 3.7%, 5.9%, and 6.0%, respectively. Notably, the QM method achieved the highest accuracy in CCS prediction, highlighting its superiority in capturing the conformational behavior of the metabolites. The semi-empirical-based engine CREST had a success rate of 50% in accurately predicting CCS values. However, the ML-based engine Auto3D exhibited a slightly lower accuracy rate of 40%. The force field-based engines Balloon and ETKDG yielded the lowest accuracy rates of 25% and 30%, respectively. These results underscore the importance of selecting appropriate conformation generation engines for accurate prediction of global minima and CCS values in molecular gas phase conformational ensembles. The QM method, with its ability to capture fine structural details and accurately calculate CCS values, emerges as the most reliable approach. The findings also highlight the promising performance of the semi empirical based engine CREST and the ML based engine Auto3D, while indicating the limitations of FF based tools such as Balloon and ETKDG in accurately representing the conformational space and predicting CCS values.