This research tackles the fundamental requirement of synthetic data generation to tighten up machine learning model precision and one-shot shot learning to lessen the need to pursue data input. The project aims to develop a service that can generate reasonable synthetic data from a given dataset. It was first designed and developed, and then the project structure was set, and libraries were chosen for predevelopment analysis. This continued development process also included subsequent phases that included dataset collecting, assessment, and iterative research. Different hyperparameters were run over multiple models to select an optimal configuration. To evaluate the model's performance over produced synthetic datasets, about 1.5 and 2 times the original, synthetic data was produced, providing a basis for a robust synthetic data generating process.