Accurate rupture risk assessment of intracranial aneurysms (IAs) requires high-fidelity 3D hemodynamic analysis particularly Wall Shear Stress (WSS) distributions in critical regions [1]. Traditional Computational Fluid Dynamics (CFD) faces prohibitive computational costs from volumetric meshing and iterative solving, rendering it impractical for time-sensitive clinical workflows. Existing DeepONet-based AI surrogates [2] exhibit critical limitations: mean relative L2 errors exceeding 40% for velocity and 50% for pressure fields, with severe over-smoothing in low-WSS regions and unstable convergence for transverse velocity components in patient-specific geometries. To address these limitations, we propose HyperFlow, a hypernetwork-based neural operator framework (Figure 1) integrating conditional modulation with coordinate-based implicit neural representations for rapid 3D hemodynamic estimation. As shown in Figure 1, the framework employs a 3D Swin Transformer encoder to extract multi-scale geometric features from vascular masks, combined with MLP-based encoding of boundary conditions (positions, normal vectors, flow rates). These multimodal features generate a unified condition vector that drives a hypernetwork to dynamically produce patient-specific LoRA parameters, modulating a globally shared implicit neural network for efficient flow prediction. Trained on the large-scale Aneumo dataset [3], HyperFlow achieves sub-second inference—orders of magnitude faster than finite volume solvers—with 30.1% error reduction versus DeepONet and 10.3% improvement over DeepONet-SwinT (Figure 2). This framework demonstrates significant potential for instantaneous 3D hemodynamic assessment, with improvements that may facilitate large-scale screening and support time-critical rupture risk evaluation in neurovascular diagnostics.