Mental health disorders impact nearly one billion people worldwide, yet stigma and insufficient awareness often prevent individuals from seeking timely professional help. The proliferation of social media platforms has introduced new opportunities for detection of mental health conditions, enabling the analysis of user-generated content to identify whether one has a mental disorder. Traditional approaches to this task have largely relied on content-based models, such as n-grams or language embeddings, which are prone to domain-specific biases and often fail to account for the emotional dynamics inherent in mental health expressions. In this work, we propose a novel framework for detecting mental disorders through the analysis of Reddit conversations, integrating both temporal textual data and emotional cues. Our model addresses the limitations of prior methods by explicitly capturing the evolving relationship between textual content and emotional expression over time. Experimental results demonstrate a significant improvement in detection accuracy compared to existing approaches, while ablation studies highlight the critical role of temporal emotional information in enhancing performance. These findings suggest that a more nuanced, emotion-aware approach offers substantial promise for advancing computational mental health diagnostics.