Health outcomes are shaped by complex dependencies among environmental exposures and built environment characteristics, in addition to socioeconomic drivers. These influencing factors vary spatially and temporally thereby adding elements of uncertainty and difficulty in elucidating their relative impacts. This study explores geospatial modeling tools towards a better understanding of multiple specific health conditions and key environmental and infrastructure factors that serve as explanatory variables when combined with sociodemographics of individuals with specific health conditions. Harris County in Texas is modeled geospatially as a testbed demonstration for seven health outcomes that include high blood pressure, asthma, COPD, stroke, cancer, diabetes, and mental health. Studied influencing factors included air pollution (PM₂.₅, ozone), housing age, impervious surface, poverty, unemployment, and health insurance coverage. Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) models were developed. Results indicated that PM2.5 exposure significantly correlated with increased cardiovascular and respiratory health outcomes, particularly at elevated concentrations. In contrast, ozone demonstrated a plateauing effect, increasing the health risks but with a diminishing impact at higher concentrations. The correlations between social disadvantage and air quality were modest, suggesting homogenous distributions of PM2.5,  and NO2 across socioeconomically variable areas, whereas ozone exposure slightly increased with higher social disadvantage. The results pointed to the criticality of understanding geospatial heterogeneities in chronic disease burdens and emphasized the importance of using spatially adaptive models to better understand drivers of health.