For attaching solid materials, friction stir welding (FSW) is a relatively novel method recently developed. Compared to fusion welding processes, it has many benefits, such as reduced distortion, porosity, shrinkage, and cracking. FSW was first used to link aluminum alloys with limited weldability, but it has since been used to join other metallic alloys and other dissimilar alloys. It is possible to fuse two plates using FSW by inserting a non-consumable rotating tool with a specifically designed pin between them and moving it along the welding line. Multiple applications in the aerospace and shipbuilding industries and the automobile sector have seen success with this approach owing to its many benefits. Computer-aided artificial neural network (ANN) modelling may be used in material science and engineering to improve the FSW process. In the same manner, as the brain processes information, ANN is a computer processing paradigm inspired by the brain's workings. There are many nerve cells in the system. ANNs, like humans, are taught by examples and maybe both a teaching and a forecasting tool. Well-trained neural networks are excellent prediction tools and can predict results for inputs it has never seen. It may therefore be considered as an approach to automating FSW. A wide range of variables influences the FSW process. To better understand the relationship between welded material's mechanical characteristics, such as ultimate tensile strength (UTS) and hardness, this study considers three parameters: tool rotation speed, welding speed, and axial force. An artificial neural network (ANN) is developed and then evaluated to determine the mechanical characteristics of welded materials.