International Journal of Scientific Research and Engineering Development

Generative Artificial Intelligence (GenAI) has emerged as a transformative area in modern AI, enabling machines to create realistic text, images, audio, and video. This paper presents a comprehensive review of three key generative paradigms: Generative Adversarial Networks (GANs), Diffusion Models, and Large Language Models (LLMs). It examines their underlying architectures, core strengths, limitations, and practical applications across various domains. GANs laid the foundation for high-quality image synthesis, while diffusion models enhanced training stability, output fidelity, and controllability. LLMs have revolutionized natural language understanding, generation, and reasoning. The study also highlights comparative performance, data requirements, and computational challenges of these models. Ethical considerations, such as bias, privacy, and misuse, are discussed alongside the opportunities they present. Finally, future research directions focus on efficiency, interpretability, multimodal integration, and responsible deployment.