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. Each case study demonstrates how generative models like Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and diffusion models can be applied to domain-specific problems, including the creation of synthetic patient records, generation of financial time series, automated quiz content generation, art and design synthesis, and simulation of satellite imagery for agriculture. By combining theoretical explanations with practical R-based workflows, this chapter bridges the gap between academic concepts and real-world implementation. The projects presented serve not only as learning tools but also as prototypes for real applications. Whether you're a researcher, data scientist, or educator, this chapter equips you with the foundational knowledge and tools to deploy generative models for diverse use cases using the R ecosystem.