Objectives To evaluate the clinical outcomes of high-intensity focused ultrasound (HIFU) ablation in patients with different subtypes of adenomyosis as classified by preoperative magnetic resonance imaging (MRI), and to explore the implications for personalized treatment planning. Design A retrospective cohort study. Participants 447 patients with adenomyosis who underwent HIFU treatment were classified according to the Kishi MRI criteria into Type I (intrinsic, n=195), Type II (extrinsic, n=92), Type III (intramural, n=50), and Type IV (full-thickness, n=110). Methods All 447 patients underwent HIFU ablation under standardized procedural protocols.Treatment parameters, non-perfused volume (NPV) ratio, relief of dysmenorrhea (assessed by NRS score), and improvement in menstrual blood volume (assessed by FPS score) were analyzed and compared across subtypes at 3 and 6 months post-treatment. Results Patients with Type IV adenomyosis had significantly larger lesion volumes and required longer treatment durations with higher energy delivery (p<0.001). The NPV ratio was highest in Type I (74.91% ± 28.34%), followed by Type III (70.34% ± 26.12%), both significantly higher than in Type II (60.01% ± 2.9%) and Type IV (65.93% ± 25.41%) (p<0.05). All subtypes demonstrated significant and sustained improvement in both dysmenorrhea and menorrhagia at follow-up (p<0.05). The comprehensive treatment success rate was highest for Type IV at both 3 (21.82%) and 6 months (27.27%). Limitations This study has several limitations. First, the retrospective design inherently introduces selection bias and limits causal inference. Second,long-term follow-up beyond 6 months was not available, precluding assessment of durability of symptom relief and recurrence rates. Conclusions High-intensity focused ultrasound (HIFU) provides effective symptom relief for all MRI-classified subtypes of adenomyosis. Treatment parameters and efficacy, however, vary by subtype. MRI classification is therefore crucial for guiding treatment and anticipating technical challenges. A personalized approach based on subtype may further optimize therapeutic outcomes.