Synthetic Data Generation in AI and Its Applications

Modern artificial intelligence systems depend on large, diverse and well-labelled datasets, yet real data is often scarce, imbalanced, expensive to annotate, or restricted by privacy regulation. Synthetic data generation (SDG) addresses these limits by producing artificial records that preserve the statistical structure of real data without copying individual records. This paper reviews the main families of SDG methods — simulation, statistical models, and deep generative models (GANs, VAEs, diffusion models) — and their applications in healthcare, finance, autonomous driving, cybersecurity and language modelling. It then evaluates four generators (Gaussian copula, Gaussian mixture model, SMOTE-style interpolation and a jittered bootstrap) on the Breast Cancer Wisconsin dataset using a unified fidelity–utility–privacy protocol averaged over ten random splits. Models trained only on synthetic data reached up to 95.56% test accuracy against 95.96% for real data. Under severe class imbalance, synthetic minority oversampling raised minority-class F1 from 85.55% to 94.01%. Interpolation and bootstrap generators, however, produced records far closer to real patients than the statistical generators, exposing a privacy trade-off. The results show that no single metric is sufficient to judge synthetic data.