Exploratory data Analysis (EDA) is a crucial part of data science. Knowing the dataset structure helps before implementing any machine learning methods through EDA procedures. Traditional EDA techniques, however, often fail to uncover meaningful insights when dealing with imbalanced datasets or limited data. The investigation in this research examines how synthetic data improves EDA operations. Using the Credit Card Fraud Detection dataset, we generate high-quality synthetic data via GANs and SDV to improve class distribution balance and statistical reliability. The research shows how synthetic data helps researchers find hidden patterns while improving class distribution balance and statistical reliability throughout exploratory analysis through visual representation analysis of both synthetic and original data sets. Our study demonstrates that synthetic data generation techniques provide an effective solution for improving EDA analysis, particularly in fraud detection domains and other data scenarios with limited or imbalanced data distributions.