The primary optimization challenge at Dallas College is improving student retention, as many students encounter financial constraints, academic struggles, and personal issues leading to dropout. According to Tinto's Model of Student Retention, students are more likely to persist in their studies if they feel integrated into the academic and social life of the institution (Tinto, 1993). Therefore, the vision is to utilize data analytics to monitor student progress and identify at-risk students early. Implementing predictive analytics models will help develop timely interventions such as personalized counseling, academic tutoring, and financial aid assistance. This data-driven approach is supported by the Theory of Planned Behavior, which emphasizes the influence of attitudes, subjective norms, and perceived behavioral control on stakeholders' acceptance and adoption of new practices (Ajzen, 1991). To implement this vision, Dallas College will need to develop a robust data infrastructure, invest in technology, and use machine learning algorithms to identify patterns indicative of students likely to drop out. Targeted interventions based on data insights will be crucial, and engaging stakeholders through collaboration and training will ensure alignment and commitment to optimization goals. The plan predicts positive outcomes, including enhanced student success, increased operational efficiency, and a competitive advantage in the educational sector. The long-term sustainability o