This study investigates the complex interplay of socioeconomic and environmental factors contributing to lung cancer rates across US counties and parishes. The primary objectives are to predict lung cancer rates based on selected socioeconomic and environmental factors and to identify spatial clusters that reflect variations in these rates. Prior studies have explored how socioeconomic variables—such as income, insurance coverage, and smoking rates—along with environmental pollution (PM 2.5), influence lung cancer rates across different regions. This study employs a multi-stage methodology using geospatial data from various US parishes. We apply Multiscale Geographically Weighted Regression (MGWR), Spatial Autoregressive Models (SAR), and Kriging techniques to predict lung cancer rates, followed by K-means clustering based on predicted values. The results are visualized using Folium, and variable significance is assessed through P-values. Our analysis reveals several key findings. Smoking levels show a strong positive correlation with lung cancer rates, particularly in regions with higher smoking rates. Exposure to PM 2.5 significantly contributes to lung cancer risk, especially in areas with higher pollution levels. Poverty has a mixed effect, with some regions showing a negative association with lung cancer rates while others show minimal impact. Insurance coverage is linked with higher lung cancer rates in certain clusters, reflecting better access to healthcare or more comprehensive cancer reporting. Income does not show a strong or consistent effect across regions. This research provides crucial insights into lung cancer’s socioeconomic equity and environmental determinants, suggesting targeted public health interventions to mitigate risks in vulnerable communities. By highlighting the significance of smoking and air quality, this study informs policymakers and health organizations on crafting effective strategies for lung cancer prevention tailored to specific...