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Knee contact pressure is a crucial factor in the knee rehabilitation programs. Although contact pressure can be estimated using finite element analysis, this approach is generally time-consuming and does not satisfy the real-time requirements of a clinical set-up. Therefore, a real-time surrogate method to estimate the contact pressure would be advantageous. This study implemented a novel computational framework using wavelet time delay neural network (WTDNN) to provide a real-time estimation of contact pressure at the medial tibiofemoral interface of a knee implant. For a number of experimental gait trials, joint kinematics/kinetics and the resultant contact pressure were computed through multi-body dynamic and explicit finite element analyses to establish a training database for the proposed WTDNN. The trained network was then tested by predicting the maximum contact pressure at the medial tibiofemoral knee implant for two different knee rehabilitation patterns; “ medial thrust ” and “ trunk sway ”. WTDNN predictions were compared against the calculations from an explicit finite element analysis (gold standard). Results showed that the proposed WTDNN could accurately calculate the maximum contact pressure at the medial tibiofemoral knee implant for medial thrust ( R M S E ¯ =1.7 MPa, N R M S E ¯ =6.2% and ρ ¯ =0.98) and trunk sway ( R M S E ¯ = 2.6 MPa, N R M S E ¯ =9.3%, ρ ¯ =0.96) much faster than the finite element method. The proposed methodology could therefore serve as a cost-effective surrogate model to provide real-time evaluation of the gait retraining programs in terms of the resultant maximum contact pressures. [ABSTRACT FROM AUTHOR]