Generative AI has opened new frontiers in data simulation and creative content generation across domains. In R, packages like keras and torch now enable building and training generative models for a variety of data types. This chapter explores five case studies of Generative AI applications in R—spanning healthcare, finance, education, design, and agriculture—each with practical R code examples, model explanations, and sample results. We demonstrate how deep generative models (e.g., Variational Autoencoders, GANs, and diffusion models) can be implemented in R to synthesize realistic data or content. Short, focused sections provide background on each use case and why generative modeling is suitable, followed by detailed R code and discussion of model architecture, evaluation techniques, and example outputs. By the end of this chapter, a reader should grasp how to apply Generative AI methods in R for diverse projects, understand the underlying model structures, and evaluate the quality of generated outputs.