Endometrial thickness in assisted reproductive techniques is one of the essential factors in the success of pregnancy. Despite extensive studies on endometrial thickness prediction, research is still needed. We aimed to analyze the impact of endometrial thickness on the ongoing pregnancy rate in couples with unexplained infertility. A total of 729 couples with unexplained infertility were included in this study. A random forest model (RFM) and logistic regression (LRM) were used to predict pregnancy. Evaluation of the performance of RFM and LRM was based on classification criteria and ROC curve, Odd Ratio for ongoing Pregnancy by EMT categorized. The results showed that RFM outperformed the LRM in IVF/ICSI and IUI treatments, obtaining the highest accuracy. We obtained a 7.7mm cut-off point for IUI and 9.99 mm for IVF/ICSI treatment. The results showed machine learning is a valuable tool in predicting ongoing pregnancy and is trustable via multicenter data for two treatments. In addition, Endometrial thickness was not statistically significantly different from CPR and FHR in both treatments.