In-silico clinical platforms have been recently used as a new revolutionary path for virtual patients (VP) generation and further analysis, such as, drug development. Advanced individualized models have been developed to enhance flexibility and reliability of the virtual patient cohorts. This study focuses on the implementation and comparison of three different methodologies for generating virtual data for in-silico clinical trials. Towards this direction, three computational methods, namely: (i) the multivariate log-normal distribution (log- MVND), (ii) the supervised tree ensembles, and (iii) the unsupervised tree ensembles are deployed and evaluated against their performance towards the generation of high-quality virtual data using the goodness of fit (gof) and the dataset correlation matrix as performance evaluation measures. Our results reveal the dominance of the tree ensembles towards the generation of virtual data with similar distributions (gof values less than 0.2) and correlation patterns (average difference less than 0.03).