Data privacy and protection are paramount in our increasingly digital world, particularly in safeguarding private information. In this era of data scarcity, we have innovatively turned to synthetic data for training AI models. We collected real-time datasets from five fields, used AI to create synthetic datasets, and conducted a comparison study. The accuracy of synthetic data is assessed by comparing the distributions of the synthetic data (shown in green) and the original data (shown in gray). We sum up the deviations across all categories for each distribution plot to get the total variation distance (TVD). The reported accuracy is then reported as 100% TVD. These accuracies are calculated for all univariate and bivariate distributions. A final accuracy score calculated as the average across all is 90% more accuracy. Five publicly available datasets taken for study purposes include diabetes prediction, biometrics for stress monitoring dataset, facial detection dataset, credit card eligibility dataset, and smartphone biometrics datasets. We created the synthetic data for the above AI model using Mostly.AI, a tool specifically designed for creating synthetic datasets. This allowed us to compare the synthetic datasets to the original datasets in terms of accuracy, correlations, univariates, bivariates, and distances. The AI models were trained to retain the probability possibilities of properties without generating duplicate versions. This process is focused on creating non-duplicative synthetic data that mimics the probability properties of the original dataset. This data will aid in training big AI models with privacy. Both the data are not duplicates. Instead, synthetic datasets created using mostly.ai are unique and maintain the original data's properties to preserve confidentiality. The synthetic data created will help train large AI models when data is scarce.