Azoospermia is a severe problem that prevents couples from having their own children through natural pregnancy. To obtain sperm from patients with azoospermia testicular sperm extraction (TESE) is required. In non-obstructive azoospermia (NOA), a particularly severe type of male infertility, patients need to undergo microdissection testicular sperm extraction (micro TESE) to collect sperm and, at 40 to 60%, the sperm retrieval success rate is not very high. Therefore, our objective in the present study was to make a model for predicting the possibility of sperm retrieval in patients with NOA before performing micro TESE, using machine learning. We retrospectively obtained data from the medical records of 430 patients who underwent micro TESE from 2011 to 2020, and used Prediction One, which does not require coding, to create the model. We successfully achieved our objective. The AUC for the AI model was 0.7246, which is acceptable. In addition, among the variables, we found that T/E2 ratios contributed most to predicting whether sperm retrieval was possible or not. T/E2 ratios have potential as a clinical predictor of sperm retrieval in NOA.