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This study aimed to develop and externally validate machine learning (ML)-based models to characterize surgical classification patterns between hysterectomy and myomectomy using fibroid characteristics and female sex hormone profiles. This multicenter study included 600 women with uterine fibroids (UFs) who presented to 3 hospitals. Of these, 362 (60.3%) underwent hysterectomy, while 238 (39.7%) underwent myomectomy. Statistical analyses and ML models were applied to both groups. ML model development was performed using individual and combined inputs of female sex hormones together with fibroid characteristics. Five ML classification algorithms were evaluated, including support vector machines, decision trees, random forests, k-nearest neighbors, and logistic regression. In total, 2555 model-input combinations were tested. The performance of the selected best-performing model was further evaluated using an independent, blinded external validation cohort comprising 30 cases. Women in the hysterectomy group had significantly higher mean age, follicle-stimulating hormone, luteinizing hormone, UF number, UF volume, uterine volume, disease duration, gravidity, parity, and prolactin (PRL) levels compared with the myomectomy group (all P < .001). In contrast, estradiol and anti-Müllerian hormone levels were significantly lower in the hysterectomy group (P < .001). Across all modeling experiments, 2012 of 2555 model-input combinations achieved perfect classification performance (accuracy = 100%) when sex hormone profiles and UF characteristics were jointly used as inputs. Models using UF number alone also demonstrated high predictive performance, with accuracy reaching up to 96%. Agreement between algorithmic predictions and final surgical decisions was observed in 97% of cases, with one discordant case identified at a clinically borderline threshold. ML models trained on hormone profiles and fibroid characteristics were able to reproduce prevailing surgical classification patterns, largely reflecting strong baseline separability driven by age- and menopause-associated hormonal profiles, with consistent performance observed in an independent blinded validation cohort. These findings support the feasibility of quantitatively modeling routine decision structures, while highlighting the need for further validation in clinically heterogeneous and ambiguous cases.