Each car body factory has more than 200 welding machines. This has led to the high cost of requiring many specimens of the construction material to be tested to achieve adequate experimental results to derive optimal parameter values. The parameter settings of each welding machine have been difficult because there are many sensitive factors. The calculations have previously been unable to be confirmed against optimal parameters. Manual calculation of welding parameters, operator experience, and technician expertise in adjusting the parameter settings have not been consistently accurate or correct. The weld quality of the RSW process has been a significant problem for the automotive industry. The 6061-T6 aluminum alloy is of light weight and has significant mechanical properties which are of interest in this research. The aluminum alloy material is a low density material with significant mechanical properties which is expected to be extensively used in the future to partially replace steel which is currently the primary production material in automobiles. It is an important process to ensure strong structural car bodies using lightweight materials to save both energy and natural resources. RSW is a rapid joining technique extensively used to join thin shell assemblies in automotive manufacturing operations. The use of RSW on lightweight aluminum alloy is increasing. The welds are done using the Resistance Spot Welding (RSW) process which is done by a computer controlled robotic welder. In automotive production, each automobile has approximately 7,000 to 12,000 spot welds. This indicates that that the application of the ANN in welding machine control is highly successful in setting the welding parameters. The achieved results of the tensile shear strength output were mean squared error (MSE) and accuracy equal to 0.054 and 95%, respectively. The results of the tensile shear strength testing and the estimated parameter optimization are applied to the RSW process. The ANN was designed and tested for predictive weld quality by using the input and output data in parameters and tensile shear strength of the aluminum alloy, respectively. Parameter prediction by the use of an artificial neural network (ANN) as a tool in finding the parameter optimization was investigated. The parameters applied to the RSW process, with aluminum alloy, are sensitive to exact measurement. An additional RSW parameter, that is, the electrical resistance of the aluminum alloy, which varies depending on the thickness of the material, is considered to be a necessary parameter. The important RSW parameters are the welding current, electrode force, and welding time. The difficulty of RSW parameter setting leads to inconsistent quality between welds. Resistance Spot Welding (RSW) is processed by using aluminum alloy used in the automotive industry.