Maritime accident prevention requires timely, interpretable risk assessment that can support operational decision-making in Vessel Traffic Service (VTS) environments. This paper proposes an integrated framework that combines Large Language Models (LLMs) with machine-learning-based risk inference to unify accident risk probability estimation and dynamic scenario generation. Heterogeneous data sources—Automatic Identification System (AIS) trajectories, meteoro-logical observations, historical accident statistics, and tribunal adjudication reports—are standardized through a two-pass LLM pipeline and aligned on a uniform spatial grid. A gradient- boosting classifier with SHAP (SHapley Additive exPlanations) based explainability identifies key risk drivers, and isotonic regression calibration produces well-calibrated, continuous risk scores. For scenario generation, a HyperGraph-based retrieval-augmented generation (RAG) knowledge base encodes multi-way relationships among accident cases, causal factors, and maritime regulations; retrieved evidence is combined with calibrated risk scores in few-shot, Chain-of-Thought (CoT) prompts to generate interpretable accident scenarios with a cause–progression–outcome structure. A web-based dashboard with on-device text-to-speech delivers grid-level risk visualization, narrative risk scenarios, and actionable navigational advisories within a unified prediction–explanation–response interface for maritime safety management.