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The need to improve predictive analytics in healthcare demands strong frameworks that would be able to interpret the complicated physiological signals well. In this study, the researcher presents a complex multi-modal emotion recognition architecture of mental health care based on EEG, ECG, and GS Rrecordings with aGPT-based NLP interface to understand brief clinical text input and self-reported emotional responses. The framework com bines sophisticated preprocessing and synchronization can be performed using cross-correlation, noise reduction using discrete wavelet transform, and event segmentation then feature extraction can be done using wavelet scattering transform and statistics. The dimensionality reduction is through two-dimensional bidirectional principal component collaborative projection as well as the use of canonical correlation analysis to make sure that fusion of features is effective. According to the experimental assessment, provision of contextual embeddings produced by GPT results in a better interpretability score and contributes to clinical reasoning, which in turn improves the healthcare decision-making. Optimized WOA-KELM model performs significantly better than the traditional classifiers like SVM, k-NN, and XGBoost as well as the standard KELM with high valence and arousal classification rates of 96.93 and 99.05 respectively. Valence and arousal are treated as binary classification tasks for emotion recognition. The GPT module is used only for post-classification interpretation and does not influence the classification performance. Also, the GPT component demonstrates the possibilities of optimized, multi-model solutions to facilitate predictive healthcare analytics meaningfully and provide credible applications in emotion-aware diagnostics, mental health monitoring, adaptive human-computer interaction, and future ways of providing real-time and personalized healthcare solutions.