Graduate admissions can be a challenging and sometimes unpredictable process, influenced by a mix of academic scores, test results, and other personal factors. In this study, we explore how machine learning (ML) can help make this process more transparent and data-driven. We test several ML models—Linear Regression, Logistic Regression, Random Forest, and XGBoost—to predict both the probability of admission and the admitted/rejected outcome. Our results show that ensemble models, particularly XGBoost, consistently provide the most accurate predictions. Beyond prediction, we also analyze which factors matter most in the admission decision, offering helpful insights for applicants and supporting committees in making fairer, evidence-based decisions. To improve clarity, we present visualizations of model performance and feature importance. Overall, this work highlights how machine learning can support graduate admissions, providing a clearer view of key factors that shape outcomes and helping applicants and decision-makers make more informed choices.