Journal of Electronic Systems Ricardo Rodríguez Jorge 16 3 2026 https://doi.org/10.6025/jes/2026/16/3/129-145 https://www.dline.info/jes/fulltext/v16n3/jesv16n3_1.pdf The increasing complexity of automotive Electronic Control Units (ECUs) demands integrated design approaches that simultaneously address electrical performance, thermal reliability, functional safety, and real time validation. However, the scarcity of public multi-physics datasets has hindered the development of AI-driven optimization frameworks capable of unifying these competing objectives. This study presents the development and comprehensive analysis of a synthetic power thermal dataset for AI-driven ECU design optimization and risk classification. The dataset, generated through physics-informed simulations, comprises over 15 electrical, thermal, and composite variables representing realistic ECU operating conditions. Through extensive statistical testing, regression modeling, and correlation analysis including Pearson and Spearman correlations, linear and polynomial regression, and multi-collinearity assessment we identify the dominant physical drivers of thermal stress and risk behavior. Results reveal that while voltage and electrical resistance exhibit negligible influence on current variation and IR drop (R² = 0.000461, p = 0.0205), Power Density, Current, and total Power are the paramount factors dictating temperature rise and thermal stress, with Current exhibiting a very strong correlation with Thermal Stress (r = 0.810). The proposed closed loop Hardware in the Loop (HIL) integrated AI pipeline incorporating multi-task learning, FPGA accelerated inference, and physics informed neural networks enables simultaneous multi class risk classification and predictive thermal modeling. This work provides a foundational benchmark for next generation ECU design, indicating that physics aware synthetic datasets, combined with hybrid AI architectures, can effectively support real time, safety compliant optimization while reducing development costs and physical prototyping requirements.