This paper is a short version of Benali et al. (2021). Most of the existing techniques work well for low-dimensional data and fail to capture complex dependencies between data dimensions. Moreover, identifying the right combination of models and their respective parameters is still an open problem. We present MTCopula, a novel flexible and extendable synthetic complex data generation approach that automatically chooses the best Copula model and the best-fitted marginals to catch the data complexity relying on Akaike Information Criterion.