Synthetic data, which mimics real data, offers significant advantages for AI and machine learning applications, particularly in overcoming the challenges of acquiring large, curated datasets. Unlike real data, synthetic data can be generated in massive quantities, is automatically annotated, and can simulate dangerous or rare events, making it especially useful in fields like autonomous vehicles and healthcare. While it allows for complete user control over simulations, synthetic data may miss certain real-world edge cases, necessitating a mix with real data in some applications. Its utilization is growing in areas such as computer vision and tabular data, with companies like Waymo using synthetic data for complex tasks like LiDAR simulations. As privacy laws restrict access to real data, synthetic data provides a viable solution without infringing on individual privacy. The development of tools and platforms, such as the Synthetic Data Vault and plugins for Unreal Engine, further facilitates the adoption of synthetic data, which is poised to play an increasingly crucial role in the advancement of AI technologies.