A compliant micro-gripper is an important factor for precision assembly, especially for small-sized objects such as the core and shaft of a DC motor used in a mobile phone vibration motor. The compliant gripper has importantly different behaviours affecting its performance quality. Hence, an effective data-driven optimization design strategy is needed to trade-off multiple performances, simultaneously. This chapter presents an optimization design method for a compliant micro-gripper that maximizes the displacement and optimizes the resonance frequency using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Jaya algorithm. This optimization process considers the length and thickness of the flexure hinge as the main design variables. The method involves collecting data through the Taguchi method, determining the weight factors for the objective functions and the parameter model based on the ANFIS. Then, the Jaya algorithm performs multi-objective optimization, improving the micro-gripper performance while ensuring durability and compliance with industrial standards. This method provides a more effective alternative to conventional design methods.