One of a vehicle's primary components, the suspension system, enhances ride comfort, maintains handling stability, and lessens vibration from road disturbances. A passive suspension system (PSS) is a traditional suspension system that has a fixed spring and damper, and its efficiency depends on these fixed parameters of the vehicle. The active suspension system (ASS) has an external active control force that is implemented parallel to a spring and a damper. Due to the drawbacks of the passive suspension system, which has fixed parameters and huge energy dissipation, researchers have recently focused on the active suspension system. But it is still a challenging problem because it is very difficult for many researchers to design the appropriate type of control. In this thesis, a bicycle car model with a driver's seat active suspension system using an ANFIS controller based on a light duty vehicle has been developed. The proposed control systems were applied to double bump sinusoidal and random type A and type C road profiles at 20 m/s and 10 m/s vehicle speeds, and their effectiveness was evaluated in terms of ride comfort and vehicle handling stability on MATLAB/Simulink. The result shows that the ANFIS controller improve ride comfort by 93.2% peak-to-peak displacement and 40.70% settling time and vehicle handling stability by 99.1% peak-to-peak displacement and 26% settling time compared to a passive suspension system. ASS based on random road type A road profile at 10 m/s has small root mean square (RMS) value which is 0.0005301m relative to PSS at 10 m/s, ASS at 20 m/s, and PSS at 20 m/s vehicle speed and also, ASS based on random road type C road profile at 10 m/s has small RMS value which is 0.002122 m relative to PSS at 10 m/s, ASS at 20 m/s, and PSS at 20 m/s vehicle speed. The result meets all the qualifications for riding comfort of the ISO 2631 standard. It is concluded that the ANFIS controller significantly improved both ride comfort and vehicle handling stability compared to the passive suspension system. Keywords: ANFIS, ASS, PSS, RMS, Lagrange equation