Ovarian　cancer　is　one　of　the　most　aggressive　gynecological　cancers　due　to　its　high　invasion　and　chemoresistance.　It　not　only　has　a　high　incidence　rate　but　also　tops　the　list　of　mortality　rates.　Its　subtle　early　symptoms　make　subsequent　diagnosis　difficult,　significantly　delaying　timely　treatment　for　patients.　Once　ovarian　cancer　reaches　an　advanced　stage,　the　complexity　and　difficulty　of　treatment　increase　substantially,　affecting　patient　survival　rates.　Therefore,　it　is　crucial　for　both　medical　professionals　and　patients　to　remain　highly　vigilant　about　the　early　signs　of　ovarian　cancer　to　ensure　timely　intervention.　In　recent　years,　ovarian　cancer　prediction　research　has　advanced,　allowing　for　the　analysis　of　the　likelihood　and　type　of　cancer　based　on　patients’　genetic　data.　With　the　rapid　development　of　machine　learning,　numerous　efficient　classification　prediction　models　have　emerged.　These　new　technologies　offer　significant　opportunities　and　potential　for　developing　ovarian　cancer　diagnostic　prediction　methods.　However,　traditional　approaches　often　struggle　to　achieve　satisfactory　classification　accuracy　in　high-dimensional　genetic　datasets　with　small　sample　sizes.　This　research　offers　a　prediction　model　utilizing　genomic　data　to　enhance　the　early　diagnosis　rate　of　ovarian　cancer,　incorporating　feature　selection,　data　augmentation　through　adversarial　conditional　generative　adversarial　networks　(AC-GAN),　and　an　extreme　gradient　boosting　(XGBoost)　classifier.　First,　we　can　simplify　the　original　genetic　dataset　through　feature　selection　methods,　removing　irrelevant　variables　and　noise,　thereby　improving　the　model’s　predictive　accuracy.　Following　dimensionality　reduction,　AC-GAN　enriches　the　data,　producing　more　realistic　genetic　samples　to　enhance　the　model’s　generalization　capacity.　Finally,　the　XGBoost　classifier　is　applied　to　classify　the　augmented　data,　achieving　efficient　predictions　for　ovarian　cancer.　These　research　findings　strongly　demonstrate　that　the　diagnostic　method　proposed　in　this　paper　has　a　significant　advantage　in　the　predictive　diagnosis　of　ovarian　cancer,　with　an　accuracy　of　99.01%　that　surpasses　the　current　technologies　in　use.　Additionally,　the　algorithm　identifies　twelve　genes　highly　relevant　to　ovarian　cancer,　providing　valuable　insights　for　physicians　during　diagnosis.　©　2024　by　the　authors.