The paper highlights the generative AI techniques for generating synthetic data for healthcare. Data plays a key role in the healthcare industry for research, treatment planning, and diagnosis. Data privacy is vital, as the patient's identity should not be revealed. Synthetic data plays an alternative by providing rich datasets without hampering the confidentiality of the patient. The basic outline of this study is to identify an alternative to creating high-quality synthetic data close to real-world healthcare data with the help of generative AI. The technique involved state–of–the–art generative models to generate synthetic datasets and evaluate their fidelity and use in different healthcare applications. The metrics for evaluation include statistical similarity to accurate data and preserving the critical pattern and the effectiveness of synthetic data in training machine learning models. The key finding of the work is that synthetic data can enhance the capability of healthcare data analytics, which provides a solution for data scarcity and privacy. The outcome is the synthetic data, which maintains the integrity of patients’ data, leading to significant tools for healthcare innovation. This work also seeks data generation through the GAN model, which is helpful for assessment and possible healthcare business applications. The data generated through AI is evaluated using the KSS statistics method. The paper discusses synthetic data for different diseases and the resemblance between the real and synthetic data using the KS mean value.