In this study, a novel approach to map forest attributes has been investigated for boreal forests in Sweden. The methodology relies on machine learning, utilizing a combination of remote sensing data and field data for both training and evaluating the proposed models. To ensure the accuracy in estimating forest attributes at any given time, the approach incorporates a broad range of available remote sensing data including airborne laser scanning (ALS) data, weekly satellite data from Sentinel-1 and Sentinel-2, and global forest map data. However, in this study focus has been on utilizing ALS data. The field data utilized in the study are derived from the Swedish National Forest Inventory and encompass measurements of key forest variables such as above-ground biomass, stem volume, basal area-weighted mean tree height, basal area-weighted mean diameter at breast height, and basal area. The potential of exporting knowledge gained from mapping Sweden to other forested landscapes such as in Latvia, using model updating with limited reference data from the new targeted area will be the next step to investigate. Here, data from Sweden were used to take the first steps towards developing a mapping methodology. The results demonstrate a promising potential of the proposed approach that will showcase new possibilities to share knowledge of updated forest mapping using the increasing flow of high-precision remote sensing data.