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.
Yuan Wang (2025) studied this question.