The identification and assessment of bundles of both soil-based ecosystem services and soil threats (ST) represent a key point in soil health monitoring, where the definition of reference thresholds constitutes the major challenge. In particular, ST bundles assessment may allow to evaluate the effects of multiple stressors on soil multifunctionality. The objective of this work is to evaluate the effects of different scenarios, such as climate change and land use changes, on the evolution of ST bundles at European scale. To achieve this objective, we propose the assessment of a ST bundle consisting of soil erosion and soil organic carbon (SOC) losses, drivers of soil degradation, under different future scenarios that consider plausible climate and land use changes. In this work we evaluated the two individuals ST using the following indicators: potential of soil losses by water erosion and SOC losses. The revised universal loss equation (RUSLE) was used to estimate the potential soil losses due to water erosion as RUSLE is able to process large-scale input data. This equation considers 5 factors: cover management factor, support practice factor, slope length factor and slope steepness, soil erodibility factor and rainfall-runoff erosivity factor. To assess SOC losses, a digital soil mapping approach using Quantile Regression Forests (QRF) was used. SOC content data were extracted from current date SoilGrids predictions to project to 2050. To fit the DSM model, SOC content was extracted across a grid of points 100 km apart for calibration of the projection model for SOC, totaling about 40,000 observations. Climate change and land use scenarios projected to the year 2050 were used. For climate change data an average composite of 3 Global Climate Models (GCMs) from the WorldClim dataset of Inter-comparison Model Projects Coupled (CMIP6) into two Shared Socio-economic Pathways (SSPs) was used: 126 and 585. Concerning to land use change, we use the projections provided by the LUISA (Land Use-based Integrated Sustainability Assessment) modeling platform. ST bundles were finally assessed using clustering approaches including k-means and model-based algorithms. In this research, we discuss the different steps of the soil threats mapping approach. We present the method of spatial distribution of bundles as a possible solution to indicate the co-occurrence of soil threats that can reduce the cultivated areas decreasing the soil health. The presented approach allows indicating hotspots where soil threats deteriorate soil health influenced by climate and land use changes. Therefore, the proposed approach is one of the solutions that can help achieve the goals of the Green Deal and the Soil Strategy for 2050.