Rapid urbanization in developing countries has produced a marked mismatch between residents’ subjective well-being (SWB) and residential environment quality (REQ). Visually appealing new urban areas do not necessarily yield higher well-being, while dense old districts do not always exhibit low well-being. To explain the emergence of this perceptual bias (PB), this study advances a dual-pathway perspective that links the visual dimension of REQ to the psychological dimension of SWB. We develop an interpretable, spatially explicit machine-learning framework that integrates social-media-derived SWB, street-view-based perceptual metrics, and multi-scale built-environment and socioeconomic indicators at the community level. Using Beijing and Nanjing—two historic Chinese cities—as contrasting cases, we examine how facility density, greenness, accessibility, and housing market conditions jointly shape PB across old cores and newly developed districts. The framework identifies nonlinear responses and planning-relevant thresholds, providing a transferable approach for diagnosing REQ–SWB divergences and prioritizing regeneration strategies. Moving beyond a single “environment → well-being” logic toward a social–spatial interaction perspective, this study offers evidence-informed guidance for well-being-oriented and context-sensitive urban renewal in historic cities and other rapidly urbanizing settings.