With increasing reliance on data analytics across industries, the bar of concern for privacy and data security has reached an all-time high. At this point, synthetic data becomes relevant as a developing solution to the anchored problems regulatory and ethical concerns cast upon organizations. Synthetic data can present statistical properties of real-world data generated by generative AI models while keeping sensitive information private. Namely, GANs and VAEs, among other advanced AI models, are potent frameworks for creating synthetic high-quality datasets. These models enable the generation of data that conciliates between privacy and utility to support analytics with privacy preservation without hurting analytical performance. Integrating differential privacy with federated learning for the generated AI further strengthens protection against data breaches and re-identification risks. However, concerns remain regarding biased generative models, ethics, and the trade-off between data fidelity and privacy. This study discusses the capabilities of generative AI in generating synthetic data, the implications for privacy-preserving analytics, ethical dilemmas, and further research. A scheme is developed to effectively utilize generative AI in this area, which again leads to a demand for more innovation and interdisciplinary work. Synthetic data creation through generative AI will revolutionize how data will be shared in the years to come securely, ethically, and efficiently for analytics in modern times.