Driving-related stress contributes to approximately 1.35 million traffic fatalities annually worldwide, necessitating innovative approaches to enhance automotive safety through real-time stress monitoring and adaptive comfort systems. This research proposes the development of AI-driven smart materials for automotive applications based on a comprehensive analysis of existing physiological databases. The methodology integrates large-scale driving stress datasets, including the MIT PhysioNet DriveDB containing physiological recordings from 17 drivers across various stress conditions, and the SHRP2 Naturalistic Driving Study encompassing over 3,400 drivers and 5 million miles of real-world driving data. Machine learning algorithms analyze heart rate variability, electromyography, and behavioral patterns to establish quantitative relationships between physiological stress indicators and optimal material property requirements. The Materials Project database, containing over 140,000 computed material properties, serves as the foundation for AI-predicted smart material compositions. Target materials include thermochromic polymers for visual stress feedback, shape memory materials for adaptive comfort adjustment, and conductive textiles for continuous physiological monitoring. Preliminary analysis demonstrates stress classification accuracy exceeding 85% using physiological parameters, with material property predictions validated against existing automotive-grade smart materials. Expected outcomes include validated AI algorithms for stress-responsive material design, optimized formulations for thermochromic, shape memory, and conductive polymer systems, and a comprehensive feasibility assessment for automotive industry implementation. This interdisciplinary approach establishes new paradigms for human-centered materials design, potentially reducing stress-related driving incidents by 15-25% through proactive comfort intervention and real-time physiological feedback systems.