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For modern vehicle development, on-board vehicle Mass and road Gradient Estimation (MGE) can offer great benefit to many sub-systems on the vehicle, such as vehicle control system, transmission control system, and active safety system etc. However, there are still several challenges that need to be solved. Firstly, thanks to good accuracy, reliability, and robustness, regression analysis-based approaches: Recursive Least Squares (RLS) and Kalman Filter (KF) are very popular for MGE, but the trade-off between estimator’s accuracy and converge time is challenging. Furthermore, depending on vehicle and powertrain types, the implementation of MGE function could be very different. It is desired to have a structured approach for various vehicle applications’ MGE development. Lastly, good reliability of MGE does not always satisfy for complicated real-world driving maneuvers and road conditions.