Based on the advantages of the cable-driven parallel robot (CDPR), we developed a cable suspension and balance system (CSBS) as a multi-purpose device for wind tunnel tests with motion control and load measurement capability. The CSBS was designed to serve as both a wind tunnel model mount and balance. This paper suggests two novel methodologies to improve the CSBS performance, one for more accurate motion control and the other for improved load measurement. The motion control correction was implemented by applying a multi-variable polynomial function that adjusts motion commands to generate the required response utilizing the measured motion data. For more accurate load measurement, a multi-layer perceptron (MLP) is proposed as a correction function. This MLP was trained to provide corrected three forces and three moments according to the reference balance data. Several motion tests, loading tests, and statistical analyses were carried out before and after correction to investigate the correction effectiveness. Differences between the motion command and response were dramatically reduced by the polynomial correction, and the MLP learning methodology effectively enhanced the load measurement accuracy of the CSBS. Using both of the suggested corrections, the CSBS was equipped with enhanced motion control and load measurement performance. Several wind tunnel tests were conducted to examine the CSBS reliability as a model mount and load measurement system, and test results showed reasonably good agreement with reference data for a cylinder model and a NACA0015 airfoil model.