To address inter-dimensional coupling errors in six-axis force sensors, a decoupling method based on an improved grey wolf optimization (IGWO) algorithm combined with a back propagation (BP) neural network is proposed. Dynamic distribution parameter adjustment, an adaptive nonlinear control factor, and an improved position update strategy are introduced to optimize the initial weights and thresholds of the BP network, thereby enhancing convergence performance and global optimization capability. A decoupling model is established using static calibration data of the six-axis force sensor and is compared with the least squares method, standard BP, GA-BP, PSO-BP, and GWO-BP approaches. Experimental results indicate that the proposed IGWO-BP method effectively reduces inter-dimensional coupling errors. The maximum relative errors of type I and type II components are 0.267% and 0.127%, respectively, demonstrating higher decoupling accuracy than the comparative methods. To evaluate practical performance, the decoupling model is implemented on an STM32F103C8T6 microcontroller, and a real-time data acquisition system consisting of an AD7606 module and a sensor calibration platform is developed. Real-time tests at a sampling frequency of 500 Hz show that the maximum relative error between the decoupled six-axis force/torque outputs and the reference loads does not exceed ±1.6%. No evident delay or data loss is observed during operation, confirming the feasibility and effectiveness of the proposed method in terms of real-time decoupling accuracy and system stability.