A novel intelligent semi-active control system for an eleven degrees of freedom passenger car’s suspension system using magnetorheological (MR) damper with neuro-fuzzy (NF) control strategy to enhance desired suspension performance is proposed. In comparison with earlier studies, an improvement in problem modeling is made. The proposed method consists of two parts: a fuzzy control strategy to establish an efficient controller to improve ride comfort and road handling (RCH) and an inverse mapping model to estimate the force needed for a semi-active damper. The fuzzy logic rules are extracted based on Sugeno inference engine. The inverse mapping model is based on an artificial neural network and incorporated into the fuzzy controller to enhance RCH. To verify the performance of the NF controller (NFC), comparisons with existing semi-active techniques are made. The typical control strategy are linear quadratic regulator (LQR) and linear quadratic Gaussian (LQG) controllers with clipped optimal control algorithm, while inherent time-delay and non-linear properties of MR damper lie in these strategies. Simulation results demonstrated that the NFC has better control performance and less control effort than the optimal in improving the service life of the suspension system and the ride comfort of a car.