Driven by shifting hydro-climatic regimes and accelerating infrastructure expansion, the ecologically fragile Taihang Mountains are experiencing severe landscape fragmentation, threatening regional habitat suitability. This study integrates multiscale spatial regression and dynamic network inference models to analyze habitat quality dynamics and their driving mechanisms from 2000 to 2020. The findings reveal that: (1) Mean habitat quality marginally declined from 0.7187 to 0.6982, driven by a significant 20.53% expansion of low-quality patches. (2) Spatial regression and network inference indicate that land-use/land-cover and terrain slope act as robust positive drivers, while gross domestic product impacts exhibit a distinct north–south bipolarity. Land-use composition serves as the primary structural determinant of habitat quality (influence coefficient: 0.877), with terrain slope rigidly constraining land-use transitions (0.676) and landscape configuration heavily governed by the landscape shape index (0.493). (3) Scenario-based simulations demonstrate that climate-driven vegetation greening alone is insufficient for habitat quality upgrades without corresponding land-use optimization. Consequently, regional management must shift from passive greening to active structural intervention, utilizing probabilistic safety thresholds to manage infrastructure expansion and fortify vital mountainous ecological barriers.