Robot-assisted minimally invasive surgery (RA-MIS) has seen growing adoption in recent years due to its advantages in precision, reduced trauma, and shorter recovery times. A critical challenge in RA-MIS, particularly in remote leader-follower robotic configurations, is the suppression of involuntary hand tremors exhibited by surgeons. These physiological tremors, often induced by fatigue, stress, or prolonged procedures, can significantly impair surgical accuracy. To ensure optimal performance, it is essential to attenuate these unwanted vibrations during surgical tasks. One conventional solution is to model the tremor as an external noise source and apply filtering techniques such as the Kalman filter to isolate and remove the noise. However, since the characteristics of hand tremors are inherently time-varying, static filtering approaches may fall short in dynamic surgical environments. To address this, we propose two adaptive methods for enhancing the Kalman filter: one based on a fuzzy inference system, and another using a reinforcement learning technique Q-learning for real-time updating of the filter’s error covariance matrix. Simulation results indicate that both approaches significantly improve tremor suppression by dynamically adjusting to variations in the signal. These adaptive filtering techniques provide a promising solution for increasing precision and stability in robotic-assisted surgical systems.