This study highlights the role and importance of advanced, machine learning-driven predictive models in enhancing the accuracy and timeliness of identifying students at-risk of negative academic outcomes in data-driven Early Warning Systems (EWS). K-12 school districts have, at best, 13 years to prepare students for adulthood and success. They cannot afford to wait for year-to-year changes when data can indicate much sooner when interventions are needed. By employing methods such as Recursive Feature Elimination (RFE) and Cross Validation on traditional and modern predictive algorithms, this study demonstrates how advanced data techniques can improve the accuracy and reliability of these systems while allowing for far more variables to be used as early warning indicators. Including a broader range of variables in EWSs allows for more nuanced risk assessments, benefiting both at-risk students and identifying those previously considered stable. This study included 42 independent variables to predict a single dependent variable: graduation outcome. By utilizing RFE instead of handpicking variables based on correlational strength, greater predictive accuracy was found, and dynamic selection at the student-level realized. Dynamic selection at the student-level in EWSs will transform how risk is assessed, reported, and acted upon in educational institutions. Ensuring a comprehensive understanding of each student's educational standing and potential. Predictive analytics is about achieving insight, and this study demonstrates that by utilizing more variables in algorithmic EWSs, unseen levels of prediction accuracy can be achieved, realizing actionable certainty. Models using RFE proved to be the most accurate, with Random Forest and AutoML approaches being particularly effective. The highest averaged cross-validated prediction accuracy was 97.59% for grade 10, 97.94% for grade 11, and 98.77% for grade 12, achieving actionable certainty in all three grade levels. Subject-specific credit achievement variables, including American Government, US History, English, and Mathematics, as well as tracking all missing required credits, showed the strongest correlations with graduation outcomes. As educational institutions adopt the innovative practices within this research, they stand to benefit from more timely and enhanced precision in identifying at-risk students, paving the way for a future where every student has the opportunity to succeed. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://bibliotheek.ehb.be:2222/en-US/products/dissertations/individuals.shtml.]