Electric three-wheelers face significant stability and roll-over challenges during high-speed manoeuvres and uneven road conditions due to their inherent design limitations. To address these issues, this study explores the use of dual rear hub motors with an electronic differential and torque vectoring strategies for improved longitudinal and lateral control. Advanced control strategies are evaluated to enhance vehicle stability, tracking performance, and passenger comfort for electric three-wheelers. The research considers five controllers, which are model-free, model-based, and data-driven such as PID, Gain-Scheduled PI, Adaptive Sliding Mode Control (SMC), Model Predictive Control (MPC), and Artificial Neural Network (ANN). All these approaches are employed to evaluate their performance across key metrics, including roll stability index, roll angle, yaw rate tracking, longitudinal speed tracking, lateral acceleration tracking, jerks, and control action fluctuations. The anticipated results suggest that the considered controllers, including MPC, ANN, Gain-Scheduled PI, and Adaptive SMC, are expected to perform well in terms of roll-over mitigation, longitudinal tracking, lateral acceleration tracking, yaw rate stability, and passenger comfort, addressing key aspects of vehicle stability and control. They offer optimal stability, smooth torque distribution, and adaptability, significantly advancing vehicle safety and performance. The comparative analysis, incorporating weighted performance metrics is expected to give insights into the performances in terms of several parameters, concludes that MPC and ANN controllers are the most effective for electric three-wheelers. These findings highlight the importance of electronic differentials and torque vectoring in mitigating roll-overs and enhancing control dynamics in electric three-wheelers.