Abstract: To enhance the preparation process of flexible pressure sensors and improve their sensitivity characteristics, objective measures must be taken. This study identified the main factors affecting performance through a sensitivity analysis model. The sensitivity performance of the flexible sensing unit was optimized by adjusting the parameters of multi-walled carbon nanotubes (MWCNTs), multilayered graphene (MLG), mixing time, and molding temperature using mechanical co-mingling. The study utilized the central combinatorial method (CCD) in design of experiments (DOE) for multi-factor experimental design. The interaction effects of the multi-factors were analyzed using response surface methodology (RSM) and support vector machine (SVR), based on single-factor analysis. Sensitivity prediction models were established accordingly. The two models were evaluated and finalized based on the coefficient of determination ( R^2 ), root mean square error (R_mse), and mean error rate (M_ae). The results indicate that the SVR model, optimized by hyperparameterization, exhibits a higher level of accuracy and predictability. The model was then iteratively optimized using the improved dung beetle optimization (IDBO) algorithm, which yielded better sensitivity performance than earlier experiments. The simulation results show a sensitivity of 0.5512 kPa⁻¹ at a uniaxial pressure of 0-30 kPa when the CNTs content is 2.3wt%, the MLG content is 1.9wt%, the mixing time is 15 min, and the molding temperature is 78°C. The experimentally verified relative error to the actual sensitivity (0.5371 kPa⁻¹) is 2.625% and the sensitivity performance of the sensing unit in this study is also at a high level when compared with similar studies. It is proved that this method helps to find the optimal sensor content ratio and preparation process, improve the experimental efficiency, save the experimental cost, and provide a new idea for the rapid preparation of high-performance capacitive flexible pressure sensing units.