Selecting the most suitable Non-Destructive Testing (NDT) method in industries such as energy, transportation, automotive, aerospace, and oil and gas significantly contributes to quality improvement, minimizing human errors, and reducing operational costs. In this study, a smart, data-driven classification model is developed using Machine Learning (ML) techniques to recommend the most appropriate Non-Destructive Testing method for weld inspection, based on technical parameters such as weld type, thickness, base material, structural complexity, and accessibility to the weld area. A dataset containing 500 real-world Non-Destructive Testing records, including Ultrasonic Testing (UT), Eddy Current Testing (ET), Magnetic Particle Testing (MT), Radiographic Testing (RT), and Liquid Penetrant Testing (PT), was collected and preprocessed through steps such as normalization, encoding of categorical features, and missing value handling. To evaluate the model, four widely used classification algorithms—Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost)—were implemented in Python. Model performance was assessed using 10-fold cross-validation to ensure robustness and generalization, and hyperparameters were tuned via a grid search strategy for optimal configuration. The results demonstrated that the XGBoost model outperformed the other classifiers, achieving 90% overall accuracy along with superior precision, recall, and F1-score values. Moreover, the model exceeded the average performance of human experts by approximately six percent in predicting the correct Non-Destructive Testing method for samples with different material types, geometries, and weld configurations. The model’s predictions were consistent with international standards such as ASME Section V (2023) and ASTM E-Series, confirming its technical reliability and compliance. The proposed approach integrates both data-driven insights and the predictive power of classification algorithms, making it an effective and practical decision-support tool for selecting Non-Destructive Testing methods in welding applications. It enhances inspection planning, reduces variability caused by human judgment, and ensures more consistent and reliable assessments across different inspection scenarios.