In high-hazard workplaces like packaging facilities, effective fire safety is critical, but conventional practices often lack immersive realism and are costly 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 VR-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 photogrammetry was used to reconstruct key industrial objects as solid, interactive meshes. An AI-based system was employed for automatic object detection using You Only Look Once version 11 (YOLOv11) and fire class prediction based on material descriptions using Bidirectional Encoder Representations from Transformers (BERT). In addition, interaction options were generated using a text generation model Fine-tuned Language Net Text-to-Text Transfer Transformer (FLAN-T5). The results indicate that the proposed framework achieves high rendering fidelity and precision, enabling efficient and scalable development of industrial safety training modules.