Additive manufacturing grants control over both the shape and properties of the product. Therefore, validating product operation necessitates predicting its properties. In this study, an optical fiber-based temperature sensor was inserted into an additively manufactured specimen, and the actual temperature was collected. A machine learning model was constructed using the collected temperature data for calibration, enabling accurate prediction of physical properties. These predicted properties were then integrated into structural analysis to assess the performance of the specimen.