This study presents optimization techniques aimed at improving the performance of magnetorheological (MR) brakes through the application of heuristic algorithms and machine learning methods. MR fluids, characterized by their rapid and reversible transition between fluid and semi-solid states under the influence of a magnetic field, are widely used in automotive systems, robotics, and vibration control applications. The research focuses on optimizing key performance metrics—namely, the electromagnetic force acting on the front of the MR fluid magnetic particle bridge and the velocity of the bridge front—using both single- and multi-objective optimization approaches. Two distinct methodologies were employed: (i) heuristic methods using an automated system based on the mathematical model of the electromagnetic system, and (ii) hybrid methods combining machine learning models with heuristic algorithms. The results demonstrate that machine learning-assisted optimization substantially reduces computational time while maintaining high predictive accuracy. Furthermore, multi-objective optimization identified optimal structural dimensions and voltage levels that achieve balanced performance across the selected criteria. These findings highlight the potential of hybrid optimization strategies for the efficient design of MR brake systems, supporting their broader integration into advanced engineering applications.