<p>This comprehensive meta-analysis investigates multimodal approaches for real-time threat detection through facial and vocal cues, with a core focus on model explainability. We implemented 12 state-of-the-art AI models from contemporary research, spanning diverse architectures including CNN-LSTM frameworks, SVM-based approaches, and sophisticated multimodal fusion techniques. Using benchmark datasets including the Real-Life Violence Situations Dataset, RAVDESS emotional speech corpus, and specialized facial expression collections, we applied multiple explainability methods (LIME, SHAP, Grad-CAM, DeepDream, and feature visualization) to provide interpretable insights into model decision-making processes. Our results demonstrate that multimodal approaches achieve superior accuracy (88–94%) compared to unimodal techniques, with attention-based fusion mechanisms proving particularly effective at dynamically weighting input modalities. Explainability analysis revealed that temporal dynamics in facial micro-expressions and spectral patterns in vocal features were the most influential threat indicators, with eye region movements (23%) and mouth tension (18%) contributing significantly to predictions. We identified critical limitations in current approaches: an overreliance on computationally expensive CNN-LSTM architectures (requiring 142.6ms inference time and 283MB RAM for the best-performing model), limited exploration of lightweight alternatives suitable for edge deployment, and insufficient attention to real-time inference paradigms essential for practical surveillance applications. This research provides valuable insights for developing more transparent, efficient, and deployable threat detection systems through explainability-aware architecture design and optimization techniques.</p>