Accurate mapping of species’ distributions and monitoring their temporal changes are critical for ecological research, biodiversity assessment, and conservation planning, especially in response to accelerating global change. Current approaches to mapping species distributions, such as expert-derived maps or species distribution models (SDMs), often face significant limitations, including taxonomic and geographic incompleteness, sampling biases, and insufficient spatial coverage. In contrast, classical species range-mapping approaches based on species records geometry, while simpler and more accessible than SDMs, often lack ecological grounding and precision. Here, we present gbif.range, an R package designed to generate ecologically informed species range maps at various geographical scales by integrating occurrence records with well-known or custom ecoregions. The package offers tailored functions for retrieving, filtering, and customizing occurrence records from the Global Biodiversity Information Facility (GBIF) and associated ecoregion layers. This streamlined workflow significantly reduces technical complexity and improves data integration, making the tool accessible to researchers with varying levels of expertise. We detail the functionalities of gbif.range and showcase its ability to infer species richness distribution at both global and regional scales. At the global scale, we validate gbif.range using data from 381 species, revealing strong concordance correlations with IUCN expert-derived diversity distributions (Lin’s ccc ~0.76) and species range areas (Lin’s ccc ~0.73). Regionally, we illustrate the package’s capacity to assess the accuracy of range maps using various validation data sources. By addressing common barriers such as technical complexity and data availability, gbif.range enables life science researchers, from novices to advanced users, to generate, refine and evaluate species distributions. With the continuous expansion of ecological data sets, gbif.range is expected to mitigate current range mapping limitations, enhancing SDMs outputs and the ecological accuracy and practical relevance of species distribution maps.