Synthetic Data refers to data that is artificially generated rather than collected from real-world observations​.The goal is to create datasets that mimic the statistical properties of real data without using actual sensitive or hard-to-obtain records. Synthetic data can be used to augment real datasets or to entirely replace them when real data is scarce, expensive to collect, or contains privacy-sensitive information​.Common methods for synthetic data generation include using statistical distributions, simulations, or generative models (such as GANs or variational autoencoders) to produce new samples. By matching the distributions and correlations of real data, synthetic data allows model training and testing in scenarios where using real data is impractical or risky. It is used in areas like healthcare (to protect patient privacy), autonomous driving (simulated driving scenarios), and machine learning model development to increase the diversity or volume of training data.