Mental health issues such as stress, depression, and anxiety have risen notably in recent years, particularly among young adults and working professionals. Conventional diagnosis often depends on self-reporting and interviews, which can be delayed due to stigma and limited access to care. This work introduces a multimodal machine learning framework that integrates text, speech, and behavioral signals for early detection of mental health patterns. Language cues are analyzed using NLP techniques, acoustic features are captured through CNN-based speech models, and behavioral interactions provide additional context. Evaluations on DAIC-WOZ, RAVDESS, and PHQ-9-annotated data demonstrate that attention-based multimodal fusion outperforms unimodal approaches while maintaining model transparency through interpretability tools.