<![CDATA[Micro, Small, and Medium-Sized Enterprises (MSMEs) are facing increasing pressure to undergo digital transformation. However, many MSMEs continue to rely on manual data management processes, making them vulnerable to human error and operational inefficiencies. This study aims to examine the benefits of implementing machine learning to automate data engineering processes in MSMEs, while identifying the key barriers to adoption and highlighting unresolved research gaps in the existing literature. A Systematic Literature Review (SLR) was conducted using the Population, Intervention, Comparison, Outcome, and Context (PICOC) framework. A total of 25 peer-reviewed studies published between 2021 and 2026 were selected from six academic databases: Google Scholar, ResearchGate, DOAJ, Garuda, Scopus, and IEEE Xplore. The synthesis identified four major benefits of machine learning implementation: reduced dependence on manual data processing, improved business prediction accuracy, operational cost savings of up to 42% through more efficient data pipelines, and more responsive inventory and customer management. Despite these advantages, adoption remains constrained by limited digital capabilities of human resources, inadequate infrastructure and financial resources, and insufficient historical data for model development. Furthermore, the literature analysis revealed that approximately 80% of existing studies focus primarily on the operational use of machine learning outputs, whereas the design of automated upstream data engineering foundations tailored to the resource constraints of MSMEs has received very limited attention. This research gap represents the primary focus and contribution of the present study.]]>
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