Welding defect prediction plays a crucial role in Industry 4.0, where quality and efficiency are essential. It requires rapid processing time and high quality right at the first stage for design and planning preparation, which are indispensable. Welding defects are difficult to detect during the welding process, but they can be predicted by using a mathematical model. This paper proposes a Neuro-Fuzzy system welding defect prediction model for the MIG welding process of aluminum 6061, comparing it with logistic regression analysis. Welding parameters are taken into account as input fuzzy membership functions. Three parameters are selected: welding voltage, welding current, and welding speed. Major defects are considered: undercut, crack, and spatter. Fuzzy rules are collected and created in the fuzzy inference system. The model was successfully tested and compared to the Nominal Logistic Regression (NLR) prediction model. The benefit and contribution of this study are in its application for learning, training, and guidance for the actual MIG welding process. The model can also be updated with additional rules and a database of welding parameters in the future. This study compares Neuro-Fuzzy and Nominal Logistic Regression for predicting welding defects in MIG welding of aluminum 6061. Both models classified three defect types. It was found that the Neuro-Fuzzy model achieved higher accuracy—approximately 96% compared to Logistic Regression at 93%. Neuro-Fuzzy handled nonlinear relationships better, making it more effective for complex welding data. Logistic Regression, while easier to interpret and implement, struggled in cases where defect probabilities were closely distributed.