The recent advancements in synthetic data generation have significantly enhanced the capabilities of AI systems, particularly in addressing data scarcity and improving fairness. Diffusion models, such as the Tabular Denoising Diffusion Probabilistic Model (Tab-DDPM), have shown promise in generating synthetic tabular data, which has been instrumental in improving AI fairness. These models are adaptable to various feature types and have been validated across multiple machine learning models, demonstrating their effectiveness in binary classification tasks. Additionally, novel approaches like the Heterogeneous Sequential Feature Forest Flow (HS3F) have addressed the limitations of existing methods by sequentially generating data and improving the handling of categorical variables, leading to faster and more robust data generation. In the realm of cybersecurity, comparative analyses have highlighted the superiority of GAN-based methods, such as CTGAN and CopulaGAN, in generating high-fidelity synthetic data for network traffic, contributing to more effective security measures. Furthermore, innovative resampling and GAN methods have been proposed to enhance the quality of catastrophic data in insurance, providing more accurate simulations and expert opinions. Overall, these developments underscore the transformative potential of synthetic data generation in advancing various fields, from AI fairness to cybersecurity and insurance.