Globally, approximately 186 million individuals grapple with infertility, a condition that brings about emotional, financial, social, and physical hardship. Over the past decade, there has been a growing utilization of in vitro fertilization (IVF) as an effective treatment for infertility. Nonetheless, despite significant advancements, the success rate remains relatively modest, with only about 30% of embryo transfers resulting in pregnancy. One contributing factor to this limited success is the absence of an efficient method for selecting high-quality embryos with a strong likelihood of developing into a baby. Traditionally, the assessment of embryo quality has relied on subjective and inaccurate morphological grading and scoring methods, which lack consistency. Numerous studies have endeavored to introduce novel strategies for embryo assessment. While microscopy-based embryo monitoring has proven beneficial, it is both costly and labor-intensive, posing challenges in routine laboratory settings. Invasive preimplantation genetic testing for aneuploidy has not yielded significant improvements in IVF success rates. Additionally, non-invasive biochemical techniques, such as microRNA and metabolic activity measurements in embryo-spent media, have shown unreliability and inaccuracy. One plausible explanation is the difficulty in measuring molecules in the minute quantities of human embryo materials, compounded by the noisy nature of acquired molecular data, which presents challenges for analysis using conventional bioinformatic tools. We have developed a highly sensitive technology capable of profiling all types of cell-free RNA secreted by embryos, encompassing both small RNA and mRNA. Furthermore, we employ artificial intelligence (AI) techniques to scrutinize this RNA data, enabling us to predict the likelihood of a successful pregnancy. This research endeavor is geared towards enhancing the non-invasive identification of high-quality embryos by merging our groundbreaking molecular technology with AI. The result will be an accurate and dependable methodology suitable for clinical routine, promising a transformative shift in modern IVF practice.