This study presents the first high-resolution (0.005°) gridded labor market data, generated by downscaling district-level census data for Ghana using random forest algorithms and remote sensing. It addresses the lack of spatially disaggregated labor market data by mapping 17 employment categories—including age, gender, skills, status, sectors, unemployment, and NEET. Auxiliary data (64 variables) such as land cover, nighttime lights, infrastructure, and points of interest are integrated to capture demographic, economic, and participation factors. The model achieves high accuracy (R2 > 90% for most categories) and reveals significant spatial heterogeneity, with employment rates ranging from 10% to 98% across pixels. Results highlight urban-rural and North-South divides, as well as sectoral concentrations. Variable importance analysis underscores the role of built-up areas, nighttime light, road density, and vegetation health in predicting employment patterns, with specificity across different employment categories. The methodology advances beyond traditional GDP or population gridding by incorporating labor market complexity. Findings demonstrate the potential of machine learning and geospatial data to enhance socio-economic mapping in data-scarce contexts.