This chapter explores the transformative role of Generative Artificial Intelligence (GenAI) in advancing synthetic dataSynthetic data generation and simulation, with a particular focus on applications in social science research. Researchers working in critical and sensitive areas, such as studies involving ethnic-minority populations in the Global SouthGlobal South, often face significant challenges related to limited, biased, or inaccessible datasetsdatasets. GenAI offers a novel approach by generating synthetic datadata that can serve as a valuable starting point for initial experimentation and methodological exploration. The chapter presents case studiesCase studies demonstrating how GenAI can support research samplingresearch sampling and simulate participant responses by adopting the personas of target populations, effectively generating context-relevant data for specific studies. It also examines the use of GenAI in the co-creation of data collection instruments, such as surveyssurveys and questionnaires, tailored to specific cultural, linguistic, or sectoral needs. This approach not only reduces development time but also enhances the usability and contextual relevance of research toolsResearch tools. While the primary emphasis is on applications within the social sciencessciences, the methods and insights presented have broader implications across a range of research domains. Finally, the chapter critically addresses the ethical dimensions of using GenAI for synthetic data simulationData simulation, including issues related to regulatory frameworks, biasbias and fairnessFairness, and the validation and reliability of AI-generated data.