ABSTRACT The geographical accessibility to health facilities is conditioned by the topography and environmental conditions overlapped with different transport facilities between rural and urban areas. To better estimate the travel time to the most proximate health facility infrastructure and determine the differences across heterogeneous land coverage types, this study explored the use of a novel cloud-based geospatial modeling approach and use as a case study the unique geographical and ecological diversity in the Peruvian territory. Geospatial data of 145,134 cities and villages and 8,067 health facilities in Peru were gathered with land coverage types, roads infrastructure, navigable river networks, and digital elevation data to produce high-resolution (30 m) estimates of travel time to the most proximate health facility across the country. This study estimated important variations in travel time between urban and rural settings across the 16 major land coverage types in Peru, that in turn, overlaps with socio-economic profiles of the villages. The median travel time to primary, secondary, and tertiary healthcare facilities was 1.9, 2.3, and 2.2 folds higher in rural than urban settings, respectively. Also, higher travel time values were observed in areas with a high proportion of the population with unsatisfied basic needs. In so doing, this study provides a new methodology to estimate travel time to health facilities as a tool to enhance the understanding and characterization of the profiles of accessibility to health facilities in low- and middle-income countries (LMIC), calling for a service delivery redesign to maximize high quality of care.