<p>Generative AI has emerged as a transformative technology in healthcare, enabling the generation of high-fidelity synthetic data for applications such as medical imaging, electronic health records, biomedical signal processing, and drug discovery. The increasing reliance on machine learning in healthcare necessitates large-scale, high-quality datasets, yet real-world data acquisition is often constrained by privacy regulations, heterogeneity, and limited accessibility. Generative AI models provide a viable solution by generating realistic and diverse synthetic datasets while preserving patient confidentiality. Unlike prior reviews that primarily focus on specific model classes or applications, this study fills a significant research gap by offering a unified, comparative evaluation of diverse generative models, including Generative Adversarial Networks, Variational Autoencoders, Transformers, and Diffusion Models, as well as their adaptations for privacy-preserving Federated Learning environments. Each model class is examined in terms of its variants, underlying methodologies, performance in healthcare applications, strengths, limitations, and computational feasibility. The study also investigates practical considerations for deploying generative AI in clinical settings, including challenges related to training stability, bias mitigation, model interpretability, and regulatory compliance. The insights from this review provide guidance for researchers and healthcare practitioners in selecting and optimizing generative AI models for medical applications, laying the foundation for future advancements in AI-driven healthcare solutions.</p>