This paper explores the recent advancements in using large language models (LLMs) for synthetic data generation, particularly in scenarios with limited data availability. It highlights the shift towards Generative Artificial Intelligence (AI) and the role of LLMs in generating task-specific training data. The authors outline methodologies, evaluation techniques, and practical applications, while discussing current limitations and suggesting future research directions. The paper begins by tracing the evolution of Generative AI from models like GANs and VAEs to the groundbreaking LLMs such as GPT-3, LLaMa, and ChatGPT. These models have significantly advanced the ability to produce coherent and contextually relevant text, making them valuable for synthetic data generation. The necessity for synthetic data is emphasized, especially in specialized and private domains where real data is scarce or sensitive. The synergy between LLMs and synthetic data generation is discussed, highlighting how LLMs can address data scarcity and privacy concerns. The paper details methods for generating synthetic data, including prompt engineering techniques and parameter-efficient task adaptation. It also covers methods for measuring the quality of synthetic datasets and effective training techniques. The applications of synthetic data are explored, focusing on low-resource tasks and practical deployment scenarios. Specific case studies in medical and educational domains are presented, demonstrating the potential of synthetic data in enhancing performance and addressing unique challenges. Finally, the paper identifies challenges in synthetic data generation, such as ensuring data correctness and diversity, addressing hallucination issues, and managing data privacy and ethical concerns. It concludes by proposing potential avenues for future research in this evolving field.This paper explores the recent advancements in using large language models (LLMs) for synthetic data generation, particularly in scenarios with limited data availability. It highlights the shift towards Generative Artificial Intelligence (AI) and the role of LLMs in generating task-specific training data. The authors outline methodologies, evaluation techniques, and practical applications, while discussing current limitations and suggesting future research directions. The paper begins by tracing the evolution of Generative AI from models like GANs and VAEs to the groundbreaking LLMs such as GPT-3, LLaMa, and ChatGPT. These models have significantly advanced the ability to produce coherent and contextually relevant text, making them valuable for synthetic data generation. The necessity for synthetic data is emphasized, especially in specialized and private domains where real data is scarce or sensitive. The synergy between LLMs and synthetic data generation is discussed, highlighting how LLMs can address data scarcity and privacy concerns. The paper details methods for generating synthetic data, including prompt engineering techniques and parameter-efficient task adaptation. It also covers methods for measuring the quality of synthetic datasets and effective training techniques. The applications of synthetic data are explored, focusing on low-resource tasks and practical deployment scenarios. Specific case studies in medical and educational domains are presented, demonstrating the potential of synthetic data in enhancing performance and addressing unique challenges. Finally, the paper identifies challenges in synthetic data generation, such as ensuring data correctness and diversity, addressing hallucination issues, and managing data privacy and ethical concerns. It concludes by proposing potential avenues for future research in this evolving field.