<![CDATA[The integration of Machine Learning (ML) in production automation has become a key driver in transforming industrial systems into smart and adaptive manufacturing environments. This study aims to analyze the role of ML in improving efficiency and accuracy within production processes. The research employs a qualitative approach with a descriptive-analytical design, using library research and document analysis of reputable scientific sources. Data were analyzed through an interactive model consisting of data reduction, data display, and conclusion drawing. The findings reveal that ML significantly enhances operational efficiency through predictive maintenance, optimized scheduling, and real-time decision-making, while also improving accuracy in quality control through advanced algorithms such as deep learning, Support Vector Machines, and Artificial Neural Networks. Furthermore, ML enables process optimization by analyzing complex production variables and identifying optimal parameters. However, challenges such as data quality, system integration, and model interpretability remain critical barriers. The study concludes that a holistic integration of ML, supported by advanced technologies such as IIoT and Digital Twin, is essential for achieving higher efficiency, improved accuracy, and sustainable competitiveness in modern industrial systems.]]>
Copyrights © 2026
