The prenatal diagnosis of placenta accreta spectrum (PAS) is essential to prevent life-threatening maternal hemorrhage. Recent deep learning models using single-modality ultrasound or magnetic resonance imaging (MRI) have achieved high accuracy, but each modality has inherent limitations: Ultrasound suffers from acoustic shadowing, MRI is prone to motion artifacts, and current methods lack mechanisms to handle missing modalities in routine workflows. To overcome these limitations, we developed a biomimetic medical sensor data fusion model that addresses these limitations through three innovations: (1) a structural prior step using morphological dilation to isolate the placental–myometrial interface, (2) a bidirectional cross-attention module that dynamically aligns acoustic and electromagnetic features, and (3) a missing-modality robust training strategy. On an external validation cohort (N = 60), the model achieved an area under the curve (AUC) of 0.889 [95% confidence interval (CI): 0.824–0.941], outperforming single-modality models (ultrasound’s AUC = 0.812; MRI’s AUC = 0.843) and previous late-fusion networks (AUC = 0.857).