Synthetic data have become an increasingly important technique in various data-driven applications. By generating data based on the properties of real data, this approach offers several advantages, including data augmentation, anonymization, semi-supervised learning and balancing unbalanced learning contexts. The generation of tabular data brings additional challenges, such as the presence of very complex between different attributes and the presence of categorical values, the value of which incorporates a semantic meaning. As a result, much scientific research in recent years has been produced, creating methodologies applicable to many contexts and with very interesting results. In this thesis, I have analyzed the state of the art, experimented with some generators and have used them in applications such as Oversampling, Privacy and Fairness.