Electrically insulating composites that are lighter, stronger, more rigid, and more durable than metals have replaced metals in numerous uses because to advancements in materials engineering. Due to the drawbacks or curb of the traditional non-destructive testing (NDT) methods, which include thermography, eddy current testing, ultrasonic, X-ray, and magnetic particles, these synthetic materials require alternative inspection procedures. Due to inadequate signal penetration, typical non-destructive inspection methods operating at low frequencies necessitate removing insulating material to permit examination. a number of high-frequency inspections Without removing the insulations, methods like the microwave approach have demonstrated successful examination in finding the problem under them. A potential method to find flaws in both metal and composite materials is the use of microwave NDT with open-ended rectangular waveguides (OERW). The microwave approach, however, confronts a number of difficulties, including inadequate spatial imaging, significant inaccuracies in defect size and depth caused by variations in stand-off distance, the best frequency point choice, and the existence of outliers in microwave measurement information. For determining corrosion beneath insulation, the microwave method in combination with machine learning strategies offers great promise and feasibility. For the purpose described here, microwave NDT using OERW in combination with strong artificial intelligence techniques has a great deal of potential and feasibility. Because the influence of artificial intelligence methods has been demonstrated in several conventional NDT techniques, combining methods of artificial intelligence with methods for signal processing is extremely likely to increase the effectiveness and resolution of the microwave NDT technique.