This study seeks to investigate the relationship between in-situ monitoring data and inspection data acquired via Non-Destructive Testing (NDT) techniques in the context of Additive Manufacturing (AM) utilizing the LDMD-Wire process. The purpose of this study is to propose a method to improve the efficacy and accuracy of the validation and certification procedures for additive manufacturing (AM) products. The study describes numerous challenges associated with the administration of high-dimensional data, such as data noise, absence of values, alignment of data, and temporal time synchronization. This study examines the relationship between in-process monitoring and post-process inspection data using various correlation methods. The primary findings indicate that correlation methods can analyze the linear correlation between in-situ monitoring and inspection data in the LDMD-Wire process. The classification model for defining meltpool behavior from the process data has reduced the computational time required to do image processing and image classification. One of the proposed regression models contributes significantly to the detection of anomalies and the prediction of essential aspects, thereby augmenting the validation and certification processes. But the study acknowledges several constraints regarding the material and geometry used in the experimental procedures. Real-world scenarios can be executed effectively using the presented models' methodologies. This study contributes significantly to the advancement of additive manufacturing (AM) technology and has potential applications in various sectors with similar characteristics and data challenges.