PT  - JOURNAL ARTICLE
AU  - Matějka, Michal
AU  - Dian, Milan
AU  - Lhota, Jan
AU  - Beran, Theodor
AU  - Hlinák, Vojtěch
TI  - Machine Learning Regression Approaches for Manufacturing Cost and Time Prediction: A Comprehensive Review
DP  - 2026 Mar 21
TA  - Manufacturing Technology Journal
PG  - 53--62
VI  - 26
IP  - 1
AID - 10.21062/mft.2026.010
IS  - 12132489
AB  - Today, machine learning regression methods are quietly but fundamentally transforming cost and time estimation in manufacturing: from early pricing to labor planning to operational order management. This survey offers a comprehensive map of approaches - from linear models, to tree ensembles (RF, GBM, XGBoost) and shallow neural networks, to multi-target and tensor regressions that can exploit data structure across BOM items and sequences of operations. With an emphasis on SME conditions, we show how to reconcile three often conflicting requirements of practice: accuracy, explainability, and integration into existing data flows (MES/ERP). The paper presents a comparative taxonomy of methods, recommended validation practices (MAE, RMSE, MAPE, R<sup>2</sup> including confidence intervals) and a pragmatic adoption trajectory: from regularized multiple regressions to tree models to multi-output formulations sharing re-presentations across operations. Consolidated findings show that modern learners consistently outperform traditional baselines when supported by careful flag engineering, drift management, and data standardization. As a major research-application contribution, we propose a unified multi-objective framework for simultaneous cost and time prediction that combines domain (queueing/simulation) features with data-driven regression to enable transparent decision making in pricing and capacity planning. The study thus creates a bridge between theory and manufacturing practice and invites the reader to systematically but achievably deploy ML in everyday decision making.