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Machine learning (ML) is a technique that learns to detect patterns and trends in data. However, the quality of reporting ML in research is often suboptimal, leading to inaccurate conclusions and hindering progress in the field, especially if disseminated in literature reviews that provide researchers with an overview of a field, current knowledge gaps, and future directions. While various tools are available to assess the quality and risk-of-bias of studies, there is currently no generalized tool for assessing the reporting quality of ML in the literature. To address this, this study presents a new screening tool called STAR-ML (Screening Tool for Assessing Reporting of Machine Learning), accompanied by a guide to using it. A pilot scoping review looking at ML in chronic pain was used to investigate the tool. The time it took to screen papers and how the selection of the threshold affected the papers included were explored. The tool provides researchers with a reliable and systematic way to evaluate the quality of reporting of ML studies and to make informed decisions about the inclusion of studies in scoping or systematic reviews. In addition, this study provides recommendations for authors on how to choose the threshold for inclusion and use the tool proficiently. Lastly, the STAR-ML tool can serve as a checklist for researchers seeking to develop or implement ML techniques effectively.