This Systematic Literature Review (SLR) analyzes the influence of Machine Learning (ML) and Big Data Analytics (BDA) on decision-making processes in several industries. The study aims to explore the potential of machine learning and big data analytics in enhancing decision-making, examining the tools and platforms used, and identifying the challenges encountered during deployment. Employing the PRISMA technique, 31 publications published from 2019 to 2024 were meticulously selected through a stringent screening process, using Scopus as the principal database. The results indicate that machine learning and big data analytics substantially enhance predictive accuracy, operational efficiency, and data privacy measures, while facilitating seamless integration with current systems. Furthermore, these technologies are becoming progressively accessible to Small and Medium Enterprises (SMEs). In the healthcare sector, machine learning models have exhibited a diagnosis accuracy of 99% in detecting breast cancer. Nonetheless, the report underscores other research deficiencies, particularly the necessity for more cost-effective solutions designed for SMEs. These limitations signify opportunities for future study to investigate ML and BDA applications in underexamined areas, such as logistics and manufacturing. This research highlights the necessity of creating economical, scalable, and industry-specific machine learning and big data analytics solutions to address existing difficulties. This systematic literature review (SLR) seeks to elucidate the function of machine learning (ML) and big data analytics (BDA) in decision-making, thereby assisting researchers and practitioners in enhancing the utilization of these technologies across many industrial applications.
Prawira et al. (2025) studied this question.