Labor cost performance index (LCPI) is an important factor of financial control and operational efficiency in construction enterprises. The study presents a model that examines labor cost performance in construction projects based on the explainable artificial intelligence (AI). A historical enterprise data of 212 real construction projects was used, including variables such as revenue, cost, project size, and financial parameters to develop and validate the model. CatBoost was identified as the most suitable prediction algorithm, achieving superior prediction accuracy (R2 = 0.9568, RMSE = 0.0073). SHAP analysis shows that financial variables, including project value, total cost, and expected profit, are the most influential factors on LCPI. These results contribute to an LCPI prediction model for construction businesses, aiming to help managers plan finances and select projects strategically with machine learning tools.