The advancement of vehicle automation and electrification requires a new generation of functional-safety strategies that extend beyond conventional quality-management (QM) approaches. This paper presents a comprehensive study on the transition from QM to Automotive Safety Integrity Level D (ASIL-D) within the ISO 26262 framework and examines how artificial intelligence (AI) technologies enhance safety, reliability, and fault tolerance in modern vehicles. Using secondary data analysis combined with thematic evaluation of ADAS, lighting, and battery management systems, the research investigates practical applications across Advanced Driver Assistance Systems (ADAS), lighting control, and battery-management systems. The findings reveal that AI improves fault-detection accuracy, predictive diagnostics, and adaptive risk mitigation, supporting more reliable safety-critical operations. However, challenges such as algorithm explainability, validation under uncertainty, and long-term assurance remain barriers to certification. The study concludes by proposing a hybrid validation model that combines deterministic FuSa principles with AI-driven verification, enabling data-centric compliance for next-generation mobility. These insights contribute to safer, smarter, and more sustainable automotive engineering practices

Automotive Functional Safety, ISO 26262, ASIL-D, Artificial Intelligence, ADAS, Battery Management System, Predictive Diagnostics, Future Mobility

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