This repository provides synthetic data generation for machine learning using state-of-the-art generative models. It implements GAN-based architectures including CTGAN for conditional tabular data generation, as well as Gaussian Mixture models for faster synthesis without GPU requirements. The package includes a Streamlit UI for low-code synthetic data generation and supports both PyTorch and TensorFlow2 backends.