In Kenya, no study has attempted to incorporate the target population, nor the availability coverage and accessibility coverage to expose hidden gaps in the provision of maternity health services that target rural and marginalized populations for a high burden, yet low resource setting such as rural Siaya County. A cross-sectional study design used publicly available geospatial data in combination with administrative ward level data from the web-based district health information software, version 2 (DHIS 2). AccessMod version 5 was used for geographic coverage analysis. ArcGIS (version 10.5) and R (version 3.5.3) sufficed for the preparation of input geospatial data and the manipulation of AccessMod results respectively. The association between the geographic coverage and skilled delivery was computed using a Zero-inflated Poisson regression model at a 95% confidence level. The findings in Siaya County revealed a higher likelihood of a skilled delivery 34% (0.34; CI: 0.339–0.347) and 16% (0.16; CI: 0.162–0.167) respectively. This likelihood is for every unit increase in the proportion of pregnant women within a one-hour geographic extent of a hospital and health center—on foot, as compared to being within a similar geographic catchment area of a dispensary 7% (0.07; CI: 0.0678–0.0723) based on motorcycle traveling time. The immediate implication is that the population coverage capacity and, by extension, quality of existing facilities offering free maternity health services increases a pregnant woman’s likelihood of utilizing skilled delivery, regardless of proximity. Future research should also consider looking at the cost-implications of scaling up existing maternity health services, albeit based on local routine health facility data.