Machine learning has revolutionized various industrial sectors by giving systems the ability to learn and make decisions based on data without the need for explicit programming. This technology plays an increasingly important role in the era of big data, helping to solve various business and operational challenges. This study aims to explore the literature related to the application of machine learning in various industrial fields, identify advantages, challenges, and direct future research. The research method in the form of a literature review was conducted by collecting and analyzing 15 international journals in the last 10 years that are relevant to this topic. The results of the study show that machine learning has a significant impact in the fields of health, finance, and transportation. Techniques such as deep learning and reinforcement learning have expanded the scope of implementation, although challenges such as interpretability, data bias, and computational requirements remain major obstacles. With continued development, machine learning opens up great opportunities for industrial transformation, although it must still pay attention to ethical and desirable aspects.
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