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Early diagnosis and quantitative assessment of pediatric hydrocephalus are critical, yet automated segmentation remains challenging due to severe anatomical deformation and substantial appearance variation across early neurodevelopment, imaging modalities, and resolutions. Current deep learning approaches are often limited to ventricle-only segmentation and tailored to specific acquisition protocols, hindering their clinical utility for comprehensive joint tissue analysis and longitudinal evaluation. We propose SynHydro, a novel biomechanics-driven simulation and domain-randomized augmentation framework. SynHydro synthesizes diverse hydrocephalus anatomies from readily available healthy tissue labels and generates corresponding MRIs with randomized image characteristics. This creates a large-scale, synthetic training dataset encompassing a wide spectrum of disease severity, MRI contrast, noise, resolution, and artifacts for joint tissue-ventricle segmentation. Extensive experiments on in-house and public datasets demonstrate that a segmentation model trained exclusively on this pure synthetic data achieves performance comparable to models trained with real hydrocephalus annotations. More importantly, SynHydro-trained models exhibit superior cross-modality and cross-resolution robustness, and reliably preserve pre-/post-operative volumetric trends. By eliminating the dependency on labor-intensive pathological annotations, SynHydro provides a powerful and practical solution for building robust, generalizable segmentation tools for pediatric hydrocephalus monitoring across the clinical imaging landscape.