In the recent past, phased array technology is one of the most important methodologies used for inspection of welding. The welding defect identification is a difficult task due to noise content and uneven illumination and contrast on phased array 2D image. Artificial Neural Network (ANN) is a recent Machine Learning (ML) technology that has been achieved a lot of attention over the recent years. The saliency feature extraction for representing image has become complex due to quality of 2D image. The proper image restoration and enhancement techniques should be applied in order to improve the quality of 2D phased array image. The 2D-Adaptive Anisotropic Diffusion Filter (2D AADF) is applied to eliminate noises such as impulse noise and speckle noise. The Adaptive Mean Adjustment-Contrast Limited Adaptive Histogram Equalization (AMA-CLAHE) is the enhancement technique that is applied to improve contrast and brightness of the phased array 2D image. The welding defect region can be exactly segmented using saliency mapping to contour boundaries of defects in welding. In this paper, a novel methodology for welding defect detection is applied based on Modified Fast Fuzzy C Means (MFFCM) clustering technique by integrating Probability Mass Function (PMF) threshold technique for higher range of efficient and accurate segmentation. The Gray Level Co-Occurrence Matrix (GLCM) and 2D Band-let Transform (2D BT) are applied to extract features on segmented image. TheRadial Bias Function Neural Network (RBFNN) classifier is one of the ANN classifier for classifying welding defects. Most of image classification techniques utilize RBFNN as they will provide great range of accuracy and precision while compared to existing techniques. The localized generation error model is implemented in RBFNN in order to minimize Mean Square Error (MSE). The efficiency and accuracy of the proposed methodology has been evaluated with the help of experimental results in terms of graphical representation and numerical analysis.