To address the dimensional accuracy challenges in investment casting of DD6 nickel-based superalloy hollow turbine blades, a multi-parameter collaborative optimization and deformation response prediction method based on response surface methodology was proposed. Using a Box-Behnken design, with pouring temperature, shell temperature, and withdrawal rate as key variables, deformation response data were obtained through numerical simulation, and a second-order model incorporating linear, interaction, and quadratic terms was established to characterize the nonlinear coupling effects of process parameters on dimensional deformation. The results indicate that withdrawal rate is the dominant factor influencing deformation, while shell temperature exhibits a pronounced “U”-shaped nonlinear trend. Significant interactions between process parameters are also observed. The constructed model demonstrates high predictive accuracy, with R2 of 0.978 and an RMSE of 0.0026 mm, and exhibits strong generalization capability, enabling the identification of optimal parameter combinations even beyond the simulated dataset. Compared with conventional orthogonal design methods, the maximum deformation of the optimized process was reduced from 0.2021 mm to 0.1905 mm, achieving an improvement of approximately 5.74%. This work provides a theoretical foundation and practical strategy for dimensional accuracy control and multi-parameter process optimization in the manufacturing of complex thin-walled castings.