International Journal of Scientific Research and Engineering Development

Multimodal generative artificial intelligence (AI) integrates text, image, audio, and structured data processing to create context-rich and semantically aligned outputs. Recent advances—including CLIP, Stable Diffusion, BLIP-2, and Flamingo—have enabled powerful cross-modal synthesis, grounding, and reasoning. In parallel, hyper-personalization represents a paradigm shift toward AI systems that adapt dynamically to individual users using demographic, behavioural, and affective signals. This literature review synthesizes state-of-the-art generative architectures, multimodal pretraining strategies, and emerging frameworks for personalized AI systems. A case study, two analytical tables, and diagram descriptions are included. Ethical considerations, evaluation frameworks, and research gaps are examined.