Recent Advances in Multimodal Deep Learning for Stress Prediction: Toward Cycle-Aware and Gender-Sensitive Health Analytics

Stress anticipation is increasingly becoming a key focus in well-being analytics, as encouraged by the surge of wearable and mobile sensing devices. Deep learning models support interpreting complex physiological and behavioral stress-related patterns, more objectively than traditional self-assessment methods. Multimodal strategies have recently linked physiological, contextual, and behavioral modalities to enhance predictive accuracy and generalisation. But most of the current frameworks do not take into consideration sex-specific and hormone-cycle differences that considerably affect response to stress. In this paper, we survey state-of-the-art multimodal deep learning for stress detection and prediction from 2020 to 2025. It studies architectural primitives like hybrid CNN-LSTM, Transformer-based fusion, and attention mechanisms (at various stages), as well as emerging fairness- and cycle-aware models. The paper also addresses the challenges of diversity in datasets, interpretability and personalization and ethical deployment. Finally, it points out directions for future research on developing biologically-informed, fair and privacy-preserving stress analytics in the context of equitable and personalized mental health monitoring.