The rapid integration of artificial intelligence (AI) into automotive systems is fundamentally reshaping vehicle architectures, driving a transition toward software-defined, zonal, and highly centralized electronic platforms. While these developments enable advanced functionalities such as autonomous driving, predictive maintenance, and intelligent energy management, they also introduce significant challenges for functional safety assurance under established standards such as ISO 26262. In particular, the non deterministic behavior of machine learning (ML) models, coupled with increasing system complexity and tight hardware–software interdependencies, limits the effectiveness of traditional rule-based and reactive diagnostic mechanisms. This article examines the role of emerging semiconductor architectures in enabling predictive safety diagnostics for AI-driven automotive systems through the systematic integration of machine learning. It synthesizes recent advances in system-on-chip (SoC) design, heterogeneous computing, safety islands, silicon lifecycle management, and secure-by-design hardware to illustrate how safety-relevant intelligence can be embedded directly at the semiconductor level. The study further analyzes ML-based diagnostic techniques—including anomaly detection, probabilistic modeling, and deep learning–based health monitoring—and evaluates their alignment with ISO 26262 safety lifecycle requirements, verification and validation practices, and assurance arguments. By bridging functional safety engineering, automotive semiconductor design, and AI-based diagnostics, the article highlights emerging design patterns and validation strategies that support proactive fault detection, early degradation awareness, and improved safety integrity. The findings underscore the necessity of cross-layer co-design approaches that integrate hardware capabilities, ML models, and safety processes to achieve robust, certifiable predictive safety in next-generation automotive systems.