Machine Learning Models in Strategic Management: A Comprehensive Review of Current Applications

This paper reviews machine learning [ML] applications in strategic management [2020-2025], which has shifted the field from intuition-based to data-driven decision-making.​ Theoretical foundations include Resource-Based View, Dynamic Capabilities Theory, Strategic Decision-Making Theory, and integrative frameworks, positioning ML as a strategic resource that boosts decision accuracy by 22% via hybrid models.​ ML applies to strategic planning [e.g., high-accuracy financial prediction with support vector machines], competitive analysis [Ridge Regression for market patterns], resource allocation [70-90% forecast accuracy in HR/finance via ensemble methods], and performance evaluation.​ Methodologies cover supervised [decision trees, random forests], unsupervised [K-means], deep learning, ensemble methods, reinforcement learning, and hybrids, each with unique strengths.​ Key challenges: technical [data quality, model interpretability], organizational [cultural resistance, skill gaps], ethical [bias, privacy], and legacy system integration.