Abstract: It is well known that large-sized nonmetallic inclusion seriously affects the mechanical properties of high-strength steels, particularly the fatigue properties. Therefore, significant efforts have been made to enhance the fatigue properties of gear steels by improving the cleanliness and, thus, reducing the size and the number of inclusions in steels. However, an effective inclusion inspection method is particularly important because of the relatively low-rate emergence of large-sized inclusions in highly clean steels. Herein, a new inclusion inspection strategy was proposed using a properly hydrogen-charged tensile specimen combined with the application of the statistics of extreme value (SEV) method, which can be used to conveniently and reliably estimate the maximum inclusion size in any volume of high-strength steel and its fatigue strength. A commercial heat of 20Cr2Ni4A gear steel with high cleanliness was used to verify the proposed method. Standard tensile specimens were quenched, tempered at low-temperature, and then properly charged with electrochemical hydrogen. It is found that there were many embrittled platforms, generally with large inclusions on the fracture surfaces of the specimens after normal tensile testing because of the trapping of the charged hydrogen around inclusion and the occurrence of hydrogen embrittlement. The size, composition, and distribution of these inclusions can be analyzed using a scanning electron microscopy, thus, the maximum inclusion size can be predicted using the SEV method. To verify the accuracy of the proposed method, additional inclusion rating methods of conventional optical metallographic observation and high-cycle fatigue testing were conducted. Using the proposed method, it was confirmed that the predicted maximum inclusion size and fatigue strength are consistent with that obtained via the rotating bending fatigue test. Therefore, the proposed method is a promising, efficient, and reliable for use in high-strength steels with high cleanliness to inspect the maximum size inclusion and predict fatigue strength.