In today’s rapidly evolving digital economy, organizations face increasing pressure to make informed, timely, and strategic decisions amid complex and dynamic market conditions. Traditional decision-making models, which often rely on historical data and linear forecasting, have become insufficient in addressing the multidimensional challenges of modern business environments. In this context, predictive analytics and machine learning (ML) are emerging as transformative tools for strategic business decision-making and sustainable competitive advantage. Predictive analytics leverages statistical algorithms, data mining techniques, and real-time data streams to anticipate future outcomes, enabling businesses to proactively address risks and identify opportunities. Meanwhile, machine learning extends the predictive power by automating model adaptation and learning from data patterns without explicit programming. This synergy empowers firms to optimize operations, personalize customer engagement, enhance financial planning, and drive innovation across industries. The application of these technologies spans various domains, including supply chain management, marketing analytics, fraud detection, and human resource planning. However, successful implementation requires not only technological infrastructure but also a strategic alignment between data capabilities and organizational goals. Moreover, ethical considerations, data governance, and model interpretability are critical to ensuring trust and accountability in decision processes. This paper explores the theoretical foundations, practical applications, and strategic implications of predictive analytics and machine learning in business. It also presents case-based evidence demonstrating their value in achieving agility, efficiency, and foresight in competitive markets. By integrating data science with business strategy, organizations can enhance their decision-making frameworks and secure long-term performance advantages in the digital era.