Aluminium alloy that is utilised in the automobile sector is used to process Resistance Spot Welding (RSW). The challenge of adjusting RSW parameters results in uneven weld quality. The welding current, electrode force, and welding duration are critical RSW characteristics. An further RSW parameter that is thought to be required is the aluminium alloy's electrical resistance, which changes according to the material's thickness. When working with aluminium alloy, the RSW process's parameters are very sensitive to precise measurement. It was looked at if parameter prediction could be done by using an artificial neural network (ANN) to discover the optimal parameter. Using the parameters and the aluminium alloy's tensile shear strength as the input and output data, respectively, the artificial neural network (ANN) was created and evaluated for predicted weld quality. The RSW procedure is implemented using the results of the estimated parameter optimisation and the tensile shear strength testing. The mean squared error (MSE) and accuracy of the tensile shear strength output were 0.054 and 95%, respectively. This suggests that the use of artificial neural networks (ANNs) in welding machine control has been very effective in determining the welding parameter.