Traumatic injuries in emergency response scenarios require rapid and reliable assessment methods capable of supporting medical prioritisation and efficient resource management. This paper presents a multispectral edge deep learning system designed for early injury detection, assistance prioritisation, and financial-resource decision support in emergency response environments. The proposed approach integrates RGB, thermal, near-infrared (NIR), depth, and contextual information within an edge-based analytical framework that combines multispectral data fusion, adaptive attention-based weighting of sensor modalities, convolutional neural networks for feature extraction and lesion segmentation, severity classification, triage index estimation, and resource allocation optimisation. The developed mathematical model describes the interaction among sensory processing, local inference, decision-making, and feedback-based model updating, and system stability is analysed using a Lyapunov-based approach under specified modelling assumptions. The system was evaluated on a simulated dataset containing N = 1000 multispectral observations, including 150 test samples for final performance assessment. The obtained results demonstrated promising performance on simulated data, achieving Accuracy = 0.912 ± 0.015, Recallcrit = 0.941 ± 0.021, Precisioncrit = 0.876 ± 0.024, and F1-score = 0.908 ± 0.018. The average edge inference delay was 425 ms, satisfying the considered real-time processing threshold of 500 ms. Compared with the baseline emergency response scenario without multispectral edge-based decision support, the proposed approach reduced triage decision errors by 18.6% and unproductive resource utilisation costs by 16.4%. The contribution of this research is the formulation of an integrated multispectral edge deep learning framework that combines injury detection, automated triage assessment, resource optimisation, and financial risk considerations within a unified decision-support architecture. The results indicate the potential of the proposed approach to improve emergency response processes. However, further validation on real-world clinical and operational datasets is required to confirm its practical applicability.