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Permanent magnet synchronous motors (PMSMs) are vital to electrified transportation due to their superior torque density, efficiency, and reliability. However, traditional workflows that rely on analytical models and finite element analysis (FEA) are highly computationally intensive, limiting their practicality for large-scale, multi-objective optimization. To address this challenge, we propose an AI driven ensemble metamodel for optimizing integrated electronic brake (IEB) motors. The framework combines Gradient Boosting Regressor (GBR), Gaussian Process Regressor (GPR), and Artificial Neural Networks (ANNs), leveraging the strengths of boosting for nonlinear regression, probabilistic modeling for uncertainty quantification, and hierarchical learning for capturing complex mappings. When embedded in the non-dominated sorting genetic algorithm III (NSGA-III), the ensemble enables simultaneous optimization of torque ripple, cogging torque, and total harmonic distortion (THD) of the back-electromotive force (back-EMF), while satisfying torque constraints. The methodology was validated on both extensive (1000-sample) and reduced (25–50 sample) datasets, confirming robust accuracy and efficiency even under data-scarce conditions. A prototype optimized motor demonstrated strong alignment between predicted and experimental performance trends.