Distortion is one of the predominant challenges concerning the quality and efficiency of a welded joint. It has been shown that distortion is significantly influenced by the weld sequence. In this context, Weld Sequence Optimization (WSO) is ideal for avoiding bottlenecks in the design stage, repairing, and overall capital expenditure in a manufacturing industry. Generally, the weld procedures are determined through experienced welders and in some cases, a plan of experimentation may be necessary. The current research is based on practical testing, computational simulations, and Artificial Neural Networks (ANN). Experiments are carried out using Gas Metal Arc Welding and the Finite Element Model (FEM) has been performed by MSc Simufact Welding software as well as it is validated by high-intensity experiments. The objective of the present research is to create and evaluate a useful method providing real-time predictions of distortion as well as WSO using hot-encoding. The generated optimal sequence from Neural network (NN) models is evaluated by performing the confirmatory test. The findings revealed that the proposed ANN method can significantly predict and optimize weld sequences for reducing distortion.