This project, conducted in partnership with a public charter school network, leverages the predictive power of machine learning models for forecasting scholarship amounts for college applicants in the year 2024. Leveraging a comprehensive dataset encompassing student-level variables such as academic performance (SAT/ACT scores, high school GPA) and socioeconomic backgrounds (family income, race), along with college-level indicators like tuition fees and room and board costs, this research aims to provide scholarship predictions for applicants across 10 universities/colleges. The methodology integrates statistical analyses and machine learning techniques, including both binary and multi-class classification models, for accurate institutional modeling and evaluation. Additionally, this project has led to the development of a dedicated website for college counselors to access model predictions for scholarships and view profiles of students with similar backgrounds for reference. This project stands to benefit thousands of college applicants within the school network by introducing an innovative approach to financial planning and support, equipping students and their families with the tools needed for more informed financial decision-making.