Business forecasting plays a critical role in strategic planning, financial management, supply chain optimization, inventory control, and market analysis. Traditional forecasting methods often struggle to manage large-scale, complex, and rapidly changing business environments. Machine Learning (ML) has emerged as a transformative technology capable of enhancing forecasting accuracy through data-driven modeling, pattern recognition, and predictive analytics. This research examines machine learning applications in business forecasting across finance, sales, demand planning, customer behavior prediction, risk management, and supply chain operations. The study reviews major machine learning algorithms, implementation frameworks, benefits, challenges, and future trends. Findings suggest that machine learning significantly improves forecasting precision, adaptability, and decision-making capabilities. However, challenges related to data quality, interpretability, ethical considerations, and implementation costs remain important concerns. The article concludes that machine learning will continue to reshape business forecasting by enabling intelligent, real-time, and predictive business strategies.