The rapid development of artificial intelligence (AI) and advanced statistical modeling has transformed business and management research, reshaping practices in finance, human resource management, operations, risk assessment, and strategic planning. This review synthesizes insights from eighteen foundational and contemporary studies spanning business analytics, AI-driven decision-making, and statistical approaches to organizational performance. From early statistical approaches such as Altman’s (1968) landmark study applied discriminant analysis to bankruptcy prediction, setting an early foundation for statistical approaches in finance and Barney’s (1991) resource-based view, to contemporary AI-driven applications in talent analytics, strategic planning, fraud detection, and digital transformation, the review demonstrates how statistical rigor and AI capabilities converge to improve decision-making and firm performance. Drawing on methodologies such as discriminant analysis, structural equation modeling, deep learning, and systematic reviews, the paper highlights the evolution from statistical transparency to AI adaptability. We conclude that combining interpretability with predictive accuracy offers the strongest path for sustainable competitive advantage.