This work presents a deep-learning-assisted, simulation-based device exploration of a dielectric-modulated nanosheet field-effect transistor (NSFET) biosensor under a HER2-equivalent effective dielectric condition. The first part establishes the device physics of a gate-all-around NSFET with an analyte-filled nanocavity, where dielectric modulation affects the transfer characteristics, threshold voltage, transconductance, and subthreshold operation. The second part proposes a physics-aware surrogate model to predict the electrical response corresponding to an uncomputed effective dielectric state, rather than to make a direct clinical screening claim. The present study does not model HER2-antibody binding, antibody functionalization, selectivity, concentration-dependent biomarker loading, nonspecific adsorption, or biological sample variability; therefore, the proposed framework should be interpreted as devicelevel dielectric-state prediction rather than a clinically validated HER2 screening platform. A multilayer perceptron model is employed to predict the transfer curve using logarithmic dielectric and current representations, while dedicated surrogate models are used for geometry-dependent analysis. The proposed surrogate framework reproduces TCAD-simulated dielectric anchor states and reduces reliance on repeated TCAD simulations for uncomputed dielectric conditions. For the surrogate-inferred HER2-equivalent effective dielectric state (k = 8) under the nominal geometry (Lcavity = 18 nm and Tcavity = 5 nm), the extracted sensing parameters indicate a threshold-voltage sensitivity of approximately 0.031, a subthreshold slope of about 62 mV/dec, and an ION/IOFF ratio of nearly 1.7×109. Further analysis of cavity thickness and cavity length shows that the framework functions as a simulation-based dielectric-spectrum and design-space screening tool. Overall, the proposed framework provides a computationally efficient route for devicelevel HER2-equivalent dielectric-state exploration; but experimental biological validation is required before practical biological or clinical applicability can be established.