Infertility is a major reproductive health issue that affects about 12% of women of reproductive age in the United States. Aneuploidy in eggs accounts for a significant proportion of early miscarriage and in vitro fertilization failure. Recent studies have shown that genetic variants in several genes affect chromosome segregation fidelity and predispose women to a higher incidence of egg aneuploidy. However, the exact genetic causes of aneuploid egg production remain unclear, making it difficult to diagnose infertility based on individual genetic variants in mother’s genome. In this study, we evaluated machine learning-based classifiers for predicting the embryonic aneuploidy risk in female IVF patients using whole-exome sequencing data. Using two exome datasets, we obtained an area under the receiver operating curve of 0.77 and 0.68, respectively. High precision could be traded off for high specificity in classifying patients by selecting different prediction score cutoffs. For example, a strict prediction score cutoff of 0.7 identified 29% of patients as high-risk with 94% precision. In addition, we identified MCM5, FGGY, and DDX60L as potential aneuploidy risk genes that contribute the most to the predictive power of the model. These candidate genes and their molecular interaction partners are enriched for meiotic-related gene ontology categories and pathways, such as microtubule organizing center and DNA recombination. In summary, we demonstrate that sequencing data can be mined to predict patients’ aneuploidy risk thus improving clinical diagnosis. The candidate genes and pathways we identified are promising targets for future aneuploidy studies.