Quantum machine learning algorithms hold promise for near-term quantum advantage, yet theirperformance under realistic hardware noise remains poorly characterized. In this work, we introduce ahybrid classical-to-quantum transfer learning architecture in which a fixed linear encoder maps twodimensional classical input data to four quantum rotation angles, which are subsequently encoded into aparameterized quantum circuit (PQC) ansatz executed on the PKTRON v3.7.3 simulation frameworkusing the PK NoisyLab 8Q virtual device. We systematically evaluate the degradation of binaryclassification accuracy and the quantum feature space under three distinct decoherence channels —amplitude damping, phase damping, and depolarizing noise — across five noise strength levels (p = 0.01to 0.35) and three entanglement depths (L = 1, 2, 3 layers). Our results reveal that phase damping anddepolarizing noise impose an immediate, strength-invariant accuracy reduction of 10% from the idealbaseline of 83.33%, while amplitude damping exhibits partial robustness at low noise levels. Frobeniusdistance analysis demonstrates that depolarizing noise induces the steepest degradation of the quantumfeature space, reaching a saturation plateau at p = 0.20. Critically, deeper entanglement circuits achievehigher ideal accuracy but exhibit superlinear growth in Frobenius distance under noise, suggesting afundamental accuracy-robustness tradeoff. We propose a Noise Sensitivity Score S(N) as a lightweight,application-level noise diagnostic metric, offering a tractable alternative to full quantum processtomography for NISQ-era devices.