Synthetic data serves as an innovative solution that tackles major problems which include privacy concerns and limited availability and unbalanced data resources. The presented pipeline “Synthetic Data Factory: Scalable and Domain-Agnostic Data Generation with Generative AI and Statistical Fidelity” delivers an extensive data generation solution. A machine learning system produces synthetic data of high quality in different domains through its implementation of CTGAN and Gaussian Copulas. The designed framework maintains identical statistical characteristics to real data by protecting practical value so it passes ML model examinations. The data generation system integrates four essential elements which consist of data preparation, synthetic data production and statistical assessment and machine learning-based utility testing. This pipeline provides scalability alongside domain adaptability along with matched performance between synthetic and original data which signifies its potential as an AI application privacy solution.