Data are most important for any manufacturing processes in decision‐making. It is important to monitor this data to improve the productivity of processes. Industrial Internet of Things (IIoT) is a very effective tool for capturing process information. This work focus on implement of IIoT in TIG welding processes. We developed an IIoT‐enabled TIG welding setup that tracks key parameters like current, travel speed, gas flow rate, and arc gap in real time. This data is sent to a cloud platform and displayed through an easy‐to‐use mobile app, giving welders and engineers clear visibility into the process. This work also explore the potential application of IIoT in inspection to improve the inspection process. By capturing and processing weld images, we could measure bead width and detect any visible surface defects using edge detection and contour analysis. Also, Mobile based application is developed to store the inspection results of LPT, UT and metallography for proper documentation and analysis. The implementation of machine learning using XGBoost algorithm is discussed to predict the mechanical properties like, Ultimate tensile strength and hardness of HAZ and weld. The model performed very well, achieving over 95% accuracy, and was further explained using SHAP tools so we could understand not just what the model predicted, but why. For example, we could see how changing travel speed or gas flow affected the final weld quality. In short, this work demonstrates how combining IIoT, machine learning, and image processing can make TIG welding smarter, more reliable, and easier to control. It turns raw.