Composite structures can fracture due to hidden flaws such as delamination, pores, and foreign inclusions. To expand the use of these structures, it is crucial to identify the shape, size, and location of these inclusions using nondestructive testing (NDT) methods. Various NDT methods, including Infrared Thermography (IRT), Terahertz, microwave, Ultrasonic, and X-ray, have been employed to assess the integrity of composite structures, though each method has its limitations. Ultrasonic testing (UT) and X-ray Computed Tomography (XCT) are two common NDT methods. XCT excels at detecting volumetric flaws, while UT provides superior contrast between defective and sound areas and is effective at detecting planar defects, such as disbonds. To overcome the limitations of individual NDT methods, data fusion from multiple sources has been utilized. This approach, previously used in medical imaging and adhesive bond inspection, aims to improve defect detection in UT inspection of an additively manufactured composite part. To achieve this, Averaging and Hadamard products, two widely used fusion algorithms, were applied to immersion UT C-scans. These C-scans were obtained by varying the transducer angle relative to the normal direction. XCT scans were used as a reference to correlate high-intensity areas in C-scans with defects. The Averaging fusion method showed promising results by combining defects identifiable in separate C-scans.