This study explores and illustrates the potential of machine learning techniques—Random Forest, XGBoost, and neural networks (MLP)—in estimating the average net salary in Romania based on macroeconomic indicators. The dataset used covers the period 1991–2024 and is employed to train a model that integrates net salary in Romania, annual inflation, and the consumer price index (CPI), along with the year as a temporal variable. The results demonstrate a high prediction accuracy (MAE: 59.47 lei, RMSE: 97.60 lei – Random Forest model), providing realistic values for future salary scenarios. The paper contributes to the integration and use of artificial intelligence methods in macroeconomic forecasting and labor market analysis. Its practical utility lies in its potential to serve as a forecasting tool for wage policies, a support for employers in budget planning, and a foundation for extending the analysis to regional or sectoral levels. Moreover, the paper offers a concrete example of how AI methods can be applied in economics, highlighting the possibility of combining real economic data with modern algorithms to produce interpretable results.