Automation, including Machine Learning (ML), is increasingly being explored to reduce the time and effort involved in evidence syntheses, yet its adoption and reporting practices remain under-examined across disciplines (e.g., health sciences, education, and policy). This review assesses the use of automation, including ML-based techniques, in 2271 evidence syntheses published between 2017 and 2024 in the , and the journals , and . We focus on automation across four review steps: search, screening, data extraction, and analysis/synthesis. We systematically identified eligible studies from the three sources and developed a classification system to distinguish between manual, rules-based, ML-enabled, and ML-embedded tools. We then extracted data on tool use, ML integration, reporting practices, motivations for (and against) ML adoption, and the application of stopping criteria for ML-assisted screening. Only 5% of studies explicitly reported using ML, with most applications limited to screening tasks. Although ~12% employed ML-enabled tools, ~90% of those did not clarify whether ML functionalities were actually utilized. Living reviews showed higher relative ML integration (15%), but overall uptake remains limited. Previous work has shown that common barriers to broader adoption included limited guidance, low user awareness, and concerns over reliability. Despite ML's potential to streamline evidence syntheses, its integration remains limited and inconsistently reported. Improved transparency, clearer reporting standards, and greater user training are needed to support responsible adoption. As the research literature grows, automation will become increasingly essential-but only if challenges in usability, reproducibility, and trust are addressed.