<p>Model-guided design of dielectric elastomer actuators (DEAs) is essential for enabling their application in soft robotics. However, current modeling methods primarily rely on the finite element method (FEM), which suffers from low computational efficiency. Additionally, the simulation-to-reality (Sim2Real) gap, mainly arising from variations in material properties and manufacturing processes, poses a significant challenge. In this work, we propose a data-driven modeling framework aimed at accurately and rapidly predicting voltage-induced displacements while minimizing the Sim2Real gap. The framework integrates a multi-layer perceptron (MLP) model, which serves as a computationally efficient surrogate for the FEM model, and a cycle-generative adversarial network (CycleGAN) model, which mitigates the Sim2Real gap by leveraging adversarial learning to process both simulation and experimental data. Dimensional analysis is performed to extend the framework’s applicability across different DEA scales. The surrogate model delivers global predictions in just 0.8 s, achieving linear coefficients of determination (<i>R</i>²) of 0.99106 for release distance prediction and 0.99375 for actuation distance prediction compared to experimental results. Our model can quickly identify the feasible range of biaxial prestretch ratios required for generating the desired deformation, thereby streamlining the design process. Finally, a soft robotic gripper is designed and fabricated, demonstrating versatile object-grasping capabilities.</p>