In order to improve instructional decision-making and academic accomplishment, this research article examines the development and public transmission of predictive models, patterns, and insights. The study looks into the possible advantages, difficulties, and effects of using data-driven methods to support evidence-based practises. A mixed-methods research approach is used, combining qualitative examination of stakeholder viewpoints with quantitative analysis of educational information. The results demonstrate how predictive models can successfully identify at-risk pupils and customise training. These models' public broadcast encourages openness, teamwork, and student agency. However, important challenges include ethical issues, data privacy, and model accuracy. The study offers suggestions for applying and improving prediction models in educational contexts and advances our understanding of data-driven decision-making in education.