This study investigates the effectiveness of business information management systems (BIMS) in cross-border real estate project management, utilizing machine learning models for performance prediction. Data from 250 valid questionnaires and 15 in-depth interviews were analyzed using multiple regression analysis, classification algorithms, and clustering analysis. The results indicate that BIMS significantly enhances project management efficiency and user satisfaction by improving system quality, information quality, and service quality. Specifically, BIMS reduces average project completion time and cost overrun rates. Commercial real estate projects reported the highest average investment at $70 million, while mixed-use projects had the longest average completion time of 25 months. Residential real estate projects achieved the highest management efficiency score of 8.0. The regression model's coefficient of determination (R²) was 0.68, the classification model achieved 85% accuracy in identifying risk factors, and clustering analysis categorized projects into high-efficiency management, risk-concentrated, and resource-intensive types. These findings highlight the substantial value of BIMS in cross-border real estate project management, providing robust management tools and decision support. However, the study's limitations include a small sample size and restricted data sources, suggesting future research should expand these areas to enhance generalizability and accuracy.This study investigates the effectiveness of business information management systems (BIMS) in cross-border real estate project management, utilizing machine learning models for performance prediction. Data from 250 valid questionnaires and 15 in-depth interviews were analyzed using multiple regression analysis, classification algorithms, and clustering analysis. The results indicate that BIMS significantly enhances project management efficiency and user satisfaction by improving system quality, information quality, and service quality. Specifically, BIMS reduces average project completion time and cost overrun rates. Commercial real estate projects reported the highest average investment at $70 million, while mixed-use projects had the longest average completion time of 25 months. Residential real estate projects achieved the highest management efficiency score of 8.0. The regression model's coefficient of determination (R²) was 0.68, the classification model achieved 85% accuracy in identifying risk factors, and clustering analysis categorized projects into high-efficiency management, risk-concentrated, and resource-intensive types. These findings highlight the substantial value of BIMS in cross-border real estate project management, providing robust management tools and decision support. However, the study's limitations include a small sample size and restricted data sources, suggesting future research should expand these areas to enhance generalizability and accuracy.