• Bhakthu Bhagyalaxmi
• Kondabathini Sarika
• Eppalapally Srilatha
• Alkanti Soujanya

Abstract : Generative artificial intelligence (GenAI) has emerged as a groundbreaking paradigm in machine learning, enabling the creation of novel, high-quality content across multiple modalities. This comprehensive review examines the architectural evolution of generative models, from early generative adversarial networks (GANs) to modern diffusion models and large language models (LLMs). We present a systematic analysis of 127 peer-reviewed publications and 23 commercial implementations, developing a novel Generative Model Evaluation Metric (GEM-IQ) framework to assess performance across fidelity, creativity, efficiency, and safety dimensions. The study highlights transformative applications in healthcare (drug discovery), creative industries (content generation), and scientific research (materials discovery), while critically analyzing ethical challenges including misinformation propagation, bias amplification, and environmental impact. Our findings suggest that while GenAI offers unprecedented productivity gains (up to 4× in creative sectors), its sustainable development requires coordinated efforts in algorithmic innovation, policy formulation, and ethical oversight. We propose a tiered regulatory framework and identify five key research directions for the responsible advancement of generative technologies.

generative AI, diffusion models, large language models, AI ethics, creative automation

"Generative Artificial Intelligence: Architectures, Applications, and Ethical Frontiers", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.b175-b182, May-2025, Available :http://www.jetir.org/papers/JETIR2505120.pdf

"Generative Artificial Intelligence: Architectures, Applications, and Ethical Frontiers", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppb175-b182, May-2025, Available at : http://www.jetir.org/papers/JETIR2505120.pdf