In high-hazard workplaces like packaging facilities, effective fire safety is critical, but conventional practices fail to recognize actual hazards and are highly expensive to implement. This paper presents a hybrid reconstruction and artificial intelligence-driven framework that can potentially be applied to build interactive virtual reality environments. The objective of this study is to develop a scalable and cost-effective Virtual Reality based fire safety training system that balances realism and interactivity. To balance visual fidelity and interactivity, a hybrid reconstruction pipeline was developed. The complex background environment was reconstructed and rendered using 3D Gaussian Splatting, while for reconstructing key industrial objects as solid and interactive meshes, photogrammetry is used. An artificial intelligence-based system has been adopted for automatic object detection using You Only Look Once version 11 (YOLOv11) and material-based hazard classification using Bidirectional Encoder Representations from Transformers (BERT). In addition, interaction options are generated using a text generation model Fine-tuned Language Net Text-to-Text Transfer Transformer (FLAN-T5). The results indicate that the proposed framework produces high rendering capabilities with high precision, enabling efficient and scalable development of industrial safety training modules.