The World Health Organization (WHO) estimates that approximately 80 million men and women worldwide, with childbearing potential, need medical assistance due to fertility difficulties, which represents approximately 15% of the population. Similarly, about 15% of couples of maternal ages in Taiwan experience infertility problems. In clinical practice, in vitro fertilization (IVF) is the primary method of artificial reproduction. Using deep learning technology and an Inception-ResNetV2 model, we can create a reliable embryo classification and prediction system, which improves the selection of high-quality embryos and enhances pregnancy success rates. The classification and prediction model achieved 80% precision, AUC = 0.88, sensitivity 73% and 88% specificity. This exceeds the statistics of the Taiwanese National Health Service, where the average pregnancy rate for IVF in 2023 was 27.8%. The results indicate that our model efficiently classifies embryos for successful implantation at a higher rate than the national statistics in Taiwan.