Automatic welding systems are widely used for high-volume production industries, where the cost of related equipment is justified by the large number of pieces to be made. Detailed movement devices are required, including predetermined welding parameter sequences and timers, to form the weld joints. Automatic gas metal arc welding processes require new mathematical models to predict optimal welding parameters for a given bead geometry to accomplish the desired mechanical properties of the weldment. The developed algorithm should be able to be employed across a wide range of material thicknesses and all welding positions, and available in analytical form to be easily applied to the welding robot with high degree of confidence in predicting bead dimensions. Therefore, this study investigated welding voltage, arc current, welding speed, contact tube weld distance, and welding angle on bead reinforcement area for automated gas metal arc welding processes using a central composite design to generate response surface methodology and artificial neural network models. Average absolute deviation was used to compare accuracy between the two models. Analysis of variance showed coefficients of determination of 0.894 and 0.948 with average absolute deviation 4.01% and 3.11% for the response surface methodology and artificial neural network models, respectively. This suggests that artificial neural network is a better modeling technique for predicting bead reinforcement area compared to response surface methodology.