This study introduces a novel framework for the early detection of anxiety and depression symptoms through the integration of Ambient Intelligence (AmI) and Multi-Agent Systems (MAS). Leveraging a Belief-Desire-Intention (BDI) reasoning mechanism, our system enables real-time monitoring and intervention with high precision. Compared to existing methods such as PMMHA, DWDM, MHL, and SMAD, the proposed methodology demonstrates significant improvements in multiple performance metrics. The system achieves an accuracy of 95%, surpassing competing approaches, and reduces latency to under 6 milliseconds for emergent decision-making. It maintains a success rate above 95% while effectively managing energy consumption, which increases non-linearly from 1.0 Joules at 100 KB to 6.1 Joules at 1000 KB of data. This scalable and adaptive approach addresses critical limitations in mental health detection, offering a reliable solution for improving mental healthcare. Future work will focus on testing the framework with publicly available mental health datasets and conducting clinical trials to further validate its efficacy.