The paper aims to address the challenges involved in the control design of Wheeled Mobile Robots (WMR) under the conditions of slip. The work is done as part of investigating the possibilities of using advancements in Machine Learning techniques in WMR with the integration of modern control strategies. The research presents a dynamic model of the robot including four slip parameters and wheel torques as input. The navigation in different environments can be affected by these slip variables resulting from disturbances including terrain dynamics. A model predictive control (MPC) is used to manipulate the torque variables according to the path and variation in both longitudinal and lateral slip values. By proposing the torque control, the work can be extended to Ground Robots with different payloads. The model can follow the reference path with linear and angular velocity constraints. It is also identified that the proposed MPC can adapt to the slip variations which are observed from the change in linear velocities and accelerations under different test values of slip variables. The slip parameters can be identified in real-time with the help of machine learning techniques and can provide a reference to the MPC model proposed. In the future, Artificial Intelligence (AI) can be integrated with control system design to address the multi-terrain navigation challenges.