This research focused on analyzing the impact of Machine Learning (ML) applied through the Scratch platform on the development of mathematical operations in secondary school students. The quantitative, descriptive, and field-based study was conducted with a sample of 30 students from public schools in educational zone three. A survey technique was used with a questionnaire validated by education experts, and the instrument’s reliability was measured with a Cronbach’s Alpha of 0.89. The results obtained from pretest and posttest, as well as the application of the t-student test and ANOVA analysis, demonstrated that the experimental group, which used Scratch with ML, showed significant improvements in their mathematical performance compared to the control group. Additionally, the impact analysis based on Cohen’s D revealed a considerable effect on academic performance, reinforcing the effectiveness of the proposal. These findings not only highlight the importance of incorporating emerging technologies in education but also suggest that the use of interactive and adaptive approaches can optimize the learning of mathematics. The research concludes that integrating ML and Scratch in mathematics education is an effective tool for enhancing students’ understanding and developing critical skills
