The exponential growth of multimodal data—including text, images, audio, video, and structured enterprise signals—demands advanced AI systems capable of not only analyzing data but also generating coherent, human-centric narratives. This paper presents a Multimodal Narrative Intelligence Engine (MNIE) that integrates creative reasoning with multimodal learning to transform complex data into structured, interpretable, and actionable insights.

The proposed framework combines large language models, vision-language architectures, and graph-based reasoning to enable cross-modal understanding and contextual synthesis. By leveraging prompt-based reasoning and reinforcement learning, the system enhances narrative coherence, factual grounding, and adaptability across diverse domains. Unlike traditional analytics systems that focus primarily on prediction accuracy, MNIE introduces a paradigm shift toward narrative intelligence, where insights are communicated through explainable, story-driven outputs that align with human cognition.

The engine is evaluated across applications such as supply chain optimization, healthcare analytics, financial risk assessment, and smart manufacturing, demonstrating improved interpretability, faster decision-making, and reduced cognitive complexity for stakeholders. Additionally, the framework incorporates responsible AI principles, including bias mitigation, privacy preservation, and human-in-the-loop validation, ensuring trustworthy and ethical deployment.

This work positions multimodal narrative intelligence as a critical advancement in next-generation AI systems, bridging the gap between data-driven analytics and human understanding, and enabling more intuitive, transparent, and impactful decision support.