Automated Machine Learning (AutoML) is today of interest to many production teams looking for faster and more robust data production (see e.g. https://youtu.be/aiM_9r5strw). Large-scale soil property mapping is challenging due to the significant computational resources required and the extensive human effort needed to locate, harmonize, and prepare data (including measurements and covariates) that align with the target spatial and temporal modeling scales. To address these challenges, we developed a modular framework EO-soilmapper that automates the workflow as much as possible (read more in: https://doi.org/10.21203/rs.3.rs-5128244/v1). Our framework introduces three main components: (1) ready-to-use EU-scale covariate layers—a comprehensive and consistent set of covariates along with the process for their preparation, (2) a harmonized EU soil property point database that integrates and quality-controls soil point data from multiple sources, and (3) “scikit-map” (https://github.com/openlandmap/scikit-map) a Python package that enables a highly automated execution pipeline, minimizing manual operation. Scikit-map supports spatial-temporal point overlay, spatial machine learning, spatial-temporal mapping, parallelized processing, etc. Together, these components streamline workflows, reduce manual input, and ensure consistency across large datasets. These tools and resources can be readily adapted for other machine learning applications in environmental modeling and mapping, further supporting the open-source soil data communities.