Modern Economics & Management Forum

Against the backdrop of financial digital transformation, machine learning technology provides an efficient solution for intelligent financial cost prediction. Its core advantage lies in improving prediction accuracy and timeliness through data mining and model training. Current applications of machine learning in financial cost prediction suffer from uneven data quality, lack of targeted model selection, insufficient feature engineering design, and poor adaptability in practical implementation, which restrict the full release of prediction performance. Based on the technical characteristics of machine learning and business requirements for financial cost prediction, this paper systematically analyzes the application status and existing dilemmas. It proposes optimization strategies from four dimensions: data governance, model optimization, feature engineering, and implementation support, providing theoretical references and practical paths for enterprises to enhance financial cost prediction capabilities and strengthen cost control.