The rapid growth of multimodal data—spanning text, images, audio, video, and structured signals—has created a need for intelligent systems capable of synthesizing information into coherent, human-understandable narratives. This paper introduces a Multimodal Narrative Intelligence Engine (MNIE), a novel AI framework that integrates creative reasoning with advanced multimodal learning to generate context-aware, interpretable, and actionable narratives. The proposed system combines large language models, vision-language transformers, and graph-based reasoning modules to fuse heterogeneous data streams and construct semantically rich storylines.

Unlike traditional analytics pipelines that focus on isolated predictions, MNIE emphasizes narrative intelligence—the ability to explain patterns, infer causal relationships, and communicate insights in a structured, story-driven format. The framework incorporates reinforcement learning and prompt-based reasoning to enhance coherence, factual grounding, and domain adaptability across applications such as supply chain intelligence, healthcare diagnostics, financial risk analysis, and smart manufacturing.

Experimental results demonstrate that MNIE improves interpretability and decision support by transforming complex data into concise narratives, reducing cognitive load for stakeholders while maintaining analytical rigor. Furthermore, the system introduces governance-aware mechanisms to mitigate bias, ensure data privacy, and support human-in-the-loop validation.

This work highlights the emerging paradigm of AI-driven narrative generation as a critical bridge between data-driven insights and human decision-making, paving the way for next-generation intelligent systems that are not only predictive but also explanatory and creative.