Despite ongoing advancements in prevention and treatment strategies, socioeconomic disparities in infectious diseases remain widespread across the United States and continue to disproportionately affect vulnerable communities. The COVID-19 pandemic brought attention to these issues of place-specific structural predisposition related to poverty, density of housing, inadequate medical care, and digital divides, which determine both disease exposure and mortality. The present narrative literature review aggregates knowledge from geospatial analytics (GA) solutions aimed at understanding and remediating these issues. Drawing from spatial epidemiology, geographic information systems (GIS), geospatial statistics techniques, ML-GIS hybrids, and GA optimization models, this review highlights how geospatial tools can be utilized to define infectious disease burden clustering in geographically stratified areas and to visualize disease burden gradients from rural to urban areas. Infectious disease burden data, coupled with advancements from remote sensing imagery and high-resolution ML models, have led to distinct increases in accuracy to accurately target infectious disease burden areas related to vaccination and rural preparedness planning; these areas can be utilized to prevent unacceptable infectious disease burden risks for anyone. Despite these advancements and ongoing research efforts, issues persist regarding data deserts, privacy issues related to geoprofiling techniques, heterogeneity in geospatial data models, and techniques related to infectious disease burden research study experiments in rural public health organizations across areas related to geospatial data storage requirements. A geospatial analytics solution inclusive and oriented towards equity can be transformative in enabling precision public health approaches directed towards disrupting infectious disease burden inequity rather than perpetuating issues of inequity.