<p>Conventional design methods are less effective in defining a globally optimal trade-off of load capacity, stiffness, and other performance parameters such as friction, vibration, and energy consumption during miniature bearing structural optimization. This paper proposes an optimal determination of the crucial structural parameter combinations of micro-deep groove ball bearings (type 688) by fusing a response surface model with an ant colony optimization algorithm (ACO). A dynamic verification and update mechanism is integrated to monitor the prediction error of the response surface model against high-fidelity finite element simulations and to reconstruct the model when the error exceeds 8%. With this approach, the method drastically lowers friction and vibration while it still keeps stiffness and stability at the same level. Five core parameters were selected in the study: roller diameter, inner and outer ring groove curvature radius, contact angle, and cage clearance. Samples were created based on a central composite design, and a high-precision quadratic response surface proxy model was developed. The entropy weight-TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method was implemented to create a multi-objective comprehensive evaluation function serving as the fitness of the ant colony algorithm to guide the global search. The results reveal that by means of this method only, in comparison with the control groups employing genetic algorithms, grid search, and sequential quadratic programming, load capacity was increased by 17.55%, friction torque was reduced by 23.34%, and RMS vibration acceleration was reduced by 31.4%. The axial and radial stiffness retention rates were 97.6% and 98.9%, respectively. The integrated framework presented herein is an effective tool for resolving multi-objective conflicts; thus, it achieves not only high precision but also high efficiency, and it is a viable technical route for the intelligent, high-performance design of miniature bearings.</p>