Today, the healthcare system is perpetually challenged to optimize limited resources, especially in catastrophic situations, highlighting their vital role in our lives while maintaining exceptional standards of patient care. Human creativity and adaptability have been crucial in addressing these challenges. As healthcare challenges grow increasingly complex and urgent, human creativity and adaptability may insufficiently ensure the requisite speed and accuracy for swift and precise detection and response. This constraint has propelled the advancement of Generative Artificial Intelligence (GenAI), which has the potential to revolutionize the healthcare sector by processing extensive data, predicting needs, formulating solutions, and fundamentally altering healthcare delivery. This chapter examines the substantial impact of GenAI on optimizing healthcare resources and improving patient care. This study investigates sophisticated generative models, including Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), which produce synthetic medical data to tackle challenges associated with data scarcity and privacy concerns. These models improve predictive analytics, facilitate personalized treatment strategies, and guarantee efficient resource allocation. Furthermore, Transformer-Based Generative Models are analyzed for their capacity to derive significant insights from Electronic Health Records (EHRs), thereby enhancing healthcare predictions and decision-making processes. This section comprehensively analyzes the potential impacts of GenAI-driven generative models on resource management and improving healthcare service quality.