This study presents a novel materials-manufacturing integrated approach to fabricate a sensor-embedded biomimetic brain simulant, designed to replicate the mechanical and structural complexity of the human brain while enabling real-time intracranial biomechanical sensing during impact-based traumatic events. Mild traumatic brain injury (mTBI) remains a major public health concern, yet its underlying biomechanical injury mechanisms are not fully understood. Research on mTBI necessitates brain simulants that faithfully replicate human brain properties, capture intracranial biomechanical responses during traumatic events, and support the development of preventive and therapeutic strategies. However, previous brain simulants often fail to represent the brain’s heterogeneous and region-specific mechanical properties and intricate neuroanatomical structures. Additionally, the brain's ultra-soft characteristics pose challenges for traditional rigid sensors, limiting the real-time acquisition of deep tissue deformations during traumatic events. To address these limitations, this study presents a systematic materials-manufacturing integrated methodology to fabricate sensor-embedded 3D brain simulants with region-specific mechanical properties and neuroanatomical structural fidelity, which comprises three key components: (1) the development of mechanical property-tunable gelatin microgel composites to achieve region-specific mechanical similarity of the brain, (2) the implementation of a nested embedded printing technique to achieve neuroanatomical structural fidelity of the brain, and (3) the development of compliant brain sensors embedded within the simulant for real-time intracranial biomechanical measurement. The resulting sensor-embedded biomimetic brain simulant enables the direct acquisition of deep-tissue biomechanical responses within this fabricated brain simulant under different traumatic loading conditions. First, region-specific mechanical properties are achieved using versatile bi-phase gelatin microgel composites. By adjusting the constitute solid-phase microgel morphology and bi-phase composite composition, Young’s modulus, storage modulus, and loss modulus variations of 380-4300 Pa, 50-4500 Pa, and 30-1000 Pa, respectively, are obtained, covering typical values of different human brain regions. This enables physiologically relevant brain biomechanics representation. Second, the nested embedded printing technique is developed to replicate the brain’s intricate 3D neuroanatomical organization. Unlike conventional layer-by-layer embedded printing, this method uses an outer-to-inner deposition sequence enabled by yield-stress fluids serving as the matrix and build materials, accurately capturing nested multilayer brain structures. The capability of this 3D printing technique is demonstrated through the successful fabrication of the brain limbic system with intricately nested structures, including the fornix, thalamus, and corpus callosum within a selected white matter region. Strategically process control results in improved shape fidelity, reducing the mean printing deviation from 10.67% to 1.40%. Third, to enable real-time intracranial biomechanical dynamics acquisition, conductive gelatin/MXene microgel composites with superior electrical conductivity are integrated as soft sensors embedded in the brain matrix. These sensors can deform compliantly with the surrounding brain matrix under impact loading. Positioned in the frontal and occipital lobes in both distributed and centralized configurations, these sensors are able to acquire biomechanical data such as stress distribution from impact-induced tissue deformation. Finally, the sensor-embedded biomimetic brain simulant, with its mechanical and structural complexity, serves as a platform for studying region-specific brain responses to controlled impact forces. The sensors enable in situ acquisition of brain regional deformation under various traumatic loading conditions, including variations in impact speed, contact duration, and impact direction, offering critical insights into mTBI mechanisms. In conclusion, the systematic approach developed in this study represents a significant advancement in brain simulant technology for mTBI research. By creating personalized, mechanically heterogeneous, and structurally representative 3D brain models with integrated soft sensors, this work opens new avenues for research in mTBI pathophysiology, injury prevention, and neurodegenerative disease modeling. Bridging material science, biofabrication, and biomechanics, this platform provides valuable insights for neuroscience, injury biomechanics, and biomedical engineering applications.