This research aims to conduct a systematic literature review (SLR) of the state of the art of Web Scraping and Machine Learning applied to decision-making, and to set a precedent for future research. As part of the methodology, research questions were posed, which will provide an idea of how these topics have been developing over the last few years. As for the selection of primary studies, a search and filtering of different researches that propose novel methods and tools using the PRISMA methodology were carried out. At the end of the systematic literature review it was found that Web Scraping has been applied to several key areas such as commerce, cybersecurity, sentiment analysis, education, and job selection, whereas Machine Learning has been utilized in areas like healthcare, prediction, financial sector, and classification tasks. The review empathizes with the importance of these technologies and suggests promising direction for future research and developments in both fields.