Soil salinization, referring to the excessive accumulation of soluble salt in soils, adversely influences nutrient cycling, microbial activity, biodiversity, plant growth and crop production thus affecting soil health and ecosystem functioning. Soil salinity quantification is a major step toward mitigation of its effects. Therefore, developing quantitative tools to predict soil salinity at regional and continental levels under different boundary conditions and scenarios is crucial for sustainable soil management and security of natural resources. This study proposes an AI-driven soil salinity quantification and projection approach focused on EU soils using a set of environmental covariates which consist of soil properties, terrain attributes, climate, and remotely sensed variables. A key aspect of this study is integration of the soil salinity point data from the LUCAS survey in the AI model, complemented by the WoSIS dataset. To improve the model performance, forward feature selection technique was applied. AI algorithms including Random Forest, LightGBM, and XGBoost were used in this study enabling us to evaluate the performance of each algorithm in predicting soil salinity across EU with the XGBoost algorithm producing the most accurate results. The output of the predictive model will be a gridded dataset illustrating the spatial and temporal (yearly) distribution of soil salinity across the EU, accompanied by the corresponding uncertainty map with the spatial resolution of 1-km. This information is crucial for identifying regions with elevated salinity levels and formulating necessary action plans to mitigate the situation.