Induction Motors (IMs), which are known for their reliable performance, Low capital cost, and minimal operational expenditures, are a key component of Electric Vehicle (EV) powertrains. Nevertheless, they are susceptible to different electrical and mechanical faults that severely impact vehicle safety and performance. This necessitates the need for robust early fault detection systems. Although Machine Learning (ML) and Artificial Intelligence (AI) outperform traditional methods in terms of fault diagnostic features and accuracy, many existing studies remain limited by using simplistic models, focusing on single-fault investigations, a small number of input features, and a lack of comprehensive validations. To address these limitations, this paper presented a robust and extensively validated ML framework for IM fault diagnosis in EV settings. In this work, a high-resolution dataset was generated using MATLAB/Simulink at sampling rate of 150 kHz. The date set comprises many fault types, including Broken Rotor Bar (BRB), Inter-Turn Short Circuit (ITSC), Over/Under Voltage (OV/ UV), and Single Phasing (SPH) faults, under different load conditions. Five ML models, comprising Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Ensemble Model, and Decision Tree (DT), were optimized via Grid Search. These models were evaluated with strict sequential data partitioning, which in turn reduced data leakage. The five ML techniques achieved near-optimal accuracy (≈100%) in classifying IM faults. As explicitly clarified in this study, this exceptional performance establishes a robust theoretical baseline; it is primarily attributed to the high separability of features extracted from the clean, noise-free, and high-fidelity Simulink environment. Consequently, these results provide a strong proof-of-concept for the proposed diagnostic framework under idealized conditions.