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In this article, the authors proposed a model predictive based sliding mode control (MPSMC) based ramp metering (RM) strategy for highway traffic control. The strategy includes sliding mode control (SMC) which makes it immune to uncertainties and provides fast responses while the addition of model predictive Control (MPC) will provide optimality. Therefore, the RM strategy will simultaneously present real-time applicability, robustness, and optimality. To provide optimality, a new quadratic objective function homogeneous to total time spent (TTS) is proposed, and for both the SMC and MPSMC based RM the authors will prove the closed-loop asymptotic global stability of the strategy in the presence of disturbance and uncertainties with the help of the proposed cost function. This objective function is based on minimizing traffic dynamic deviations from desired values. The proposed RM control strategy was applied to M62 smart highway in England and results are compared with existing control measures in the highway, ALINEA and SMC-based RM. The simulations show that MPSMC will approach optimal behavior and thus, outperforms ALINEA and SMC regarding queue length and average speed. Moreover, the SMC and MPSMC based RMs are innately robust against disturbances, therefore, both will provide better performance regarding congestion reduction compared with ALINEA. Finally, it will be shown that MPSMC can improve TTS performance by 39% with respect to ALINEA while presenting global asymptotic stability.