The increasing prevalence of mental health disorders, such as chronic stress and anxiety, emphasizes the critical need for advanced monitoring systems that can provide actionable insights into psychological well-being. This study presents a novel ring-based biosensor platform that integrates multi-modal data analysis with Tiny Machine Learning (TinyML) for real-time stress and mental health assessment. The proposed system analyzes key physiological and biochemical markers of stress, including electrodermal activity through galvanic skin response (GSR) sensors, heart rate variability (HRV) for autonomic nervous system analysis, and cortisol levels as a primary stress biomarker. TinyML models embedded in the ring enable efficient on-device processing of biosensor data, identifying trends and patterns in stress markers while minimizing power consumption. This approach allows the system to deliver timely alerts for potential stress or anxiety episodes and provide personalized interventions, such as guided relaxation exercises. The localized computation ensures enhanced data privacy, low latency, and reduced reliance on external cloud services. Designed to be lightweight and ergonomic, the ring is optimized for continuous wear, making it suitable for long-term monitoring and for the early detection of stress-related conditions. Validation of the platform is conducted using established TinyML performance metrics, including sensitivity, specificity, latency, and memory footprint, ensuring reliable and efficient operation in a resource-constrained wearable device. This work demonstrates the potential of combining wearable biosensors with embedded machine learning to advance personalized mental health management and stress mitigation.