The study aims to evaluate the potential health risks associated with outdoor thermal environments within the context of global climate change and rapid urbanization. Furthermore, it seeks to propose a scientifically rigorous quantitative assessment methodology along with multi-scale adaptation strategies. Employing bibliometric analysis and comparative case studies, the research encompasses three core domains: The study aims to evaluate the potential health risks associated with outdoor thermal environments within the context of global climate change and rapid urbanization. Furthermore, it proposes a scientifically rigorous quantitative assessment methodology, complemented by multi-scale adaptation strategies. Through bibliometric analysis and comparative case studies, the research encompasses three core domains. (1) Quantitative modeling of thermal environments and individual-difference analysis, utilizing in-situ monitoring, wind-tunnel experiments, and CFD–WRF coupled numerical simulations. These methods are compared using indices such as UTCI, RHSI, and TIHR to contrast measured and simulated surface temperature fields and spatiotemporal heat-exposure distributions in representative cities including Dalian, Beijing, and Tianjin.; (2) Co-optimization strategies for healthy buildings and outdoor microclimates, which integrate urban-scale elements such as green corridors and water networks with building-scale features including form, green roofs, and passive ventilation through the application of multi-objective optimization algorithms. Additionally, these strategies are validated for their cooling performance and cost-effectiveness within prototypical street canyon contexts. (3) Analysis of population physiological variability and regional adaptability, integrating gender, age, and geographic differences to develop targeted thermal-environment regulation strategies and assess adaptive requirements and vulnerability across demographic groups. Regarding the approach to mapping methodology, within the framework of a Local Climate Zone, we undertake a comparison of pixel-level remote sensing, object-based image analysis, and deep-learning semantic segmentation techniques. This process involves implementing efficient multi-source, cross-scale data fusion through cloud platforms such as Google Earth Engine. Following over a decade of persistent research, our team has achieved a series of innovations in the field of healthy, culminating in the development of a health-oriented thermal-environment evaluation framework. By formulating multidimensional quantitative models, our scope has broadened from urban heat island assessments to encompass the synergistic mechanisms of spatial morphology and building layout, while also incorporating analyses of physiological variations. Building upon these achievements, the current study further explores the nexus between the thermal environment and human health, aiming to construct refined assessment models and climate-adaptive optimization strategies to offer data-driven decision support for mitigating health risks associated by global warming.