Remote sensing techniques are widely used for mapping and monitoring forest attributes, providing valuable information on forest cover, biomass, and overall forest health. In recent years, national airborne laser scanning (ALS) campaigns have been conducted in several countries to map forest resources. When combining ALS with field inventory data, these datasets enable the development of nationwide models for prediction of forest attributes. In this study, we explore the potential of machine learning (ML) to enhance existing modeling approaches for nationwide forest attribute mapping in Sweden. We achieve this by relating ALS data from the most recent ALS campaign of Sweden with field data from the Swedish National Forest Inventory (NFI). By aggregating laser metrics from surveyed areas (NFI plots), as well as over surrounding areas to the plots, we investigate (1) if ML approaches can outperform existing linear regression baseline models and (2) if further enhancements of the predictive capacity can be achieved by including surrounding, spatially correlated ALS data. To this end, we used extreme gradient boosting (XGBoost), as well as a convolutional neural network (CNN), specialized to handle tabular data and spatially correlated data, respectively. The models were evaluated on five forest variables: basal-area weighted mean tree height, basal-area weighted mean stem diameter, basal area, stem volume, and above-ground biomass. All models were evaluated on several nested datasets to assess the robustness, showcasing consistent results across datasets. We achieved significant improvements in prediction accuracy across all investigated forest variables. Furthermore, incorporating surrounding information to the modeling rendered further improvements for diameter, basal area, and biomass predictions. The approaches tested and developed here thus form a promising basis for flexible modeling approaches that can be transferred globally for large-scale forest monitoring and management.