The global manufacturing industry is undergoing a technological revolution driven by Industry 4.0. Among the enabling technologies, Machine Learning (ML) has emerged as a powerful tool for process optimization, enabling factories to become smarter, adaptive, and data-driven. ML algorithms can analyze massive datasets from sensors, machines, and production lines to identify patterns, predict outcomes, and optimize operations.This paper explores how machine learning enhances process optimization in smart manufacturing. It discusses ML models, implementation frameworks, benefits, challenges, and real-world applications across industries such as automotive, electronics, and metal fabrication. Quantitative results from case studies and industrial reports indicate that ML integration leads to 15–30% improvement in production efficiency, 20–40% reduction in defects, and up to 25% energy savings. These outcomes demonstrate that ML-driven optimization not only enhances productivity but also drives sustainability and adaptive decision-making in modern manufacturing