In general, low cycle fatigue evaluation of nuclear reactor components requires strain-controlled fatigue test data such as using strain versus life (e-N) curves. Conducting strain-controlled fatigue tests under in-air condition is not an issue. However, controlling strain in a PWR water test is a challenge, since an extensometer cannot be placed in a narrow autoclave (typically used in a high-temperature-pressure PWR-water loop). This is due to lack of space inside an autoclave that houses the test specimen. In addition, installing a contact-type extensometer in the path of a high-pressure flow can be a challenge. These difficulty of using an extensometer in a PWR-water loop led us to use an outside-autoclave displacement sensor which measures the displacement of pull-rod-specimen assembly. However, in our study (based on in-air fatigue test data), we found that a pull-rod-controlled based fatigue test can lead to substantial cyclic hardening/softening resulting substantially different cyclic strain amplitudes and its rates compared to the desired cyclic strain amplitudes and its rates. In this paper, we propose an AI/ML based technique such as using k-Mean clustering technique to improve the pull-rod-control based fatigue test method, such that the gauge-area strain amplitude and rates can reasonably be achieved. In support of this we present the fatigue test results for both 316 SS base and 81/182 dissimilar-metal-weld specimens.