Generative AI for Synthetic Data Generation in Machine Learning: A Review and Evaluation Framework

Machine learning models need large, representative, labeled datasets, but real data are often scarce, imbalanced, costly to label, or restricted by privacy rules. Generative AI can produce synthetic data that keeps the statistical structure of a source dataset without reusing its records, although memorization remains a risk. This paper reviews four generator families: generative adversarial networks, variational autoencoders, diffusion models, and language-model-based tabular generators. It organizes evaluation along three axes: fidelity to real data, downstream utility for model training, and privacy leakage. A worked example shows how synthetic minority samples can rebalance a skewed dataset. The paper then proposes an evaluation protocol that combines train-on-synthetic, test-on-real experiments, differential-privacy accounting, and membership-inference testing. No measured performance result is claimed. The contribution is a structured comparison and an evaluation plan for researchers and practitioners.