Synthetic data is artificially generated information that is not derived from direct measurement of real-world phenomena, created using algorithms, models, or simulations to mimic the statistical characteristics and structure of actual data. As a research concept and methodological approach, it investigates techniques for algorithmic data generation, evaluates the properties and fidelity of synthetic datasets compared to real data, and explores their application in contexts where real data is scarce, sensitive, or costly. Its key characteristics include artificial origin, potential for large-scale and controlled generation, and independence from real-world observation constraints, making it significant for training machine learning models, testing algorithms, and enabling simulations across diverse scientific and industrial domains.