Existing approaches for thermal management in data centers rely predominantly on computational fluid dynamics (CFD) simulations or facility-level machine learning models, which can be computationally demanding or may not explicitly represent rack-level airflow–thermal relationships. To address this challenge, this study proposes a predictive modeling framework for estimating rack-level thermal behavior from airflow and spatial characteristics using machine learning techniques. The analysis uses a reproducible synthetic dataset of 300 observations incorporating airflow velocity, airflow distribution index, and rack position. Linear Regression, Random Forest, and XGBoost models were evaluated using repeated 5-fold cross-validation with 10 repetitions, hyperparameter optimization, feature engineering, and an independent test set of 60 observations. Feature engineering introduced nonlinear and pairwise interaction terms among the airflow and spatial variables. The results show that Linear Regression with engineered features achieved the best independent-test performance, with R²=0.9392 and RMSE = 0.8538 °C. The engineered representation improved Linear Regression performance relative to the baseline, while providing a small training–test R² gap of 0.0046. The results demonstrate the potential of combining airflow characteristics, spatial information, and engineered features for reproducible rack-level thermal prediction. Although the dataset is synthetic, the complete generation procedure was successfully reproduced, providing a basis for future validation using larger experimental, CFD derived, or operational datasets. The proposed framework therefore provides a data-driven foundation for future predictive thermal monitoring and distribution-aware cooling strategies in data center environments.

machine learning, airflow distribution, data center cooling, rack-level thermal prediction, thermal management