Abstract
The modern corporate environment is increasingly characterized by intense social dynamics, where workplace peer pressure acts as a significant catalyst for mental disorders such as depression, anxiety, and stress-related trauma. Early detection and intervention are crucial for occupational health, yet quantifying the psychological toll of peer-induced stress remains a complex challenge. This paper explores a comprehensive artificial intelligence (AI) statistical framework designed to analyze and detect mental disorders specifically triggered by workplace peer pressure. By integrating multimodal deep learning, emotion-based modeling, and synthetic data generation, we outline a robust pipeline capable of evaluating subtle behavioral and linguistic cues associated with occupational distress. We propose a methodology that extracts salient features from text and audio modalities, utilizes emotion regulation difficulties as an intermediate metric, and applies advanced statistical modeling to classify psychological states. Furthermore, this study extensively discusses the practical implications, ethical risks regarding employee privacy, and inherent limitations of deploying such computer-aided screening systems in corporate settings. Ultimately, this work provides a foundational roadmap for developing privacy-preserving, culturally aware AI tools that can mitigate the escalation of workplace-induced mental health crises.