Background: Abnormal uterine bleeding (AUB) caused by adenomyosis can result in significant iron-deficiency anemia. Given that transvaginal ultrasound (TVUS) is commonly performed during routine examinations, its assessment may facilitate preemptive treatment. Methods: In this study, TVUS images from patients with surgically confirmed adenomyosis were preprocessed to minimize intra- and inter-scan variability. Radiomics features were extracted using PyRadiomics to train automated machine-learning classifiers under k-fold nested cross-validation. Multiple dataset configurations were evaluated, including feature extraction from a custom region-of-interest (ROI) of the uterine corpus, whole-image features adjusted for uterine size, and supplementary variables from complete blood counts (CBC). Results: Radiomics-centered models demonstrated modest discrimination (mean accuracy 0.606–0.618). While incorporating CBC variables with radiomics features substantially improved performance compared to radiomics alone, models trained exclusively on CBC data yielded overall higher results. Conclusions: These findings indicate that quantitative features derived from routine TVUS provide modest complementary information for predicting anemia. While they do not offer clear incremental value when highly discriminatory clinical data (CBC) are available, TVUS radiomics may serve as a supplementary diagnostic tool in settings where blood tests are incomplete or unavailable. Further validation in larger, multi-institutional cohorts with patient-level separation is warranted.