Limited availability and class imbalance in tabular data make it difficult the development of effective models using machine learning methods. Synthetic data generation is a promising solution, but requires rigorous methodologies and extensive evaluation. This work proposes a methodology for generating and evaluating synthetic tabular data, especially from imbalanced datasets, illustrated with a case study in Intensive Care Units. The methodology comprises: (1) class balancing by oversampling, selecting the best technique with a quality metric; (2) synthetic data generation from the optimal balanced dataset, using SMOTE RSB* Adapted with Gaussian Noise, CTGAN and TVAE; and (3) a novel quality metric, TabDSFidelity, that integrates distributional similarity, correlation preservation, and predictive utility to guide the selection at each stage. Applied to ten datasets, the methodology demonstrated that the use of synthetic data selected by a quality metric significantly improves the performance of classification models compared to using only the original data. SMOTE RSB* Adapted with Gaussian Noise consistently generated the highest quality data according to TabDSFidelity in this study. It is concluded that the proposed methodology offers an effective framework for mitigating data sparsity and imbalance, facilitating the creation of more accurate and robust models.