Dark matter (DM) continues to evade direct detection, but neutron stars (NSs) serve as natural laboratories where even a modest DM component can alter their structure. While many studies have examined DM effects on NSs, they often rely on specific choices of equations of state (EOS) models, assume isotropy, and lack a Bayesian statistical framework, limiting their predictive power. In this work, we present a Bayesian framework that couples pressure-anisotropic nuclear EOS to a self-interacting fermionic DM component, constrained by NICER and GW170817 data. Our results show that DM mass fractions up to $\sim 10\%$ remain consistent with current data, which softens the high-density EOS, leading to reduced stellar radii and tidal deformabilities while requiring negligible pressure anisotropy. Bayesian model comparison reveals no statistically significant preference between pure baryonic and DM-admixed NSs, indicating that DM inclusion enhances physical realism without complexity penalties. However, existing data cannot tightly constrain the DM parameters, and our empirical radius definition introduces a systematic bias toward the DM core configurations. To address this, we therefore introduce the DM radius span $\mathrm{\Delta }{R}_{\chi }\equiv R_{\chi ,\max }-R_{\chi ,\min }$ as a unified diagnostic for DM distributions. This parameter simultaneously characterizes core-halo transition features while exhibiting strong linear correlations ($\mathrm{\Delta }{R}_{\chi }<4\mathrm{km}$) with both DM and baryonic parameters, providing a clear avenue for future constraints. Our approach bridges current limitations and future potential in probing DM through compact star observations.

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• Dark matter
• Neutron stars & pulsars
• Bayesian methods