Recent advances in data science, statistics, and machine learning have opened new possibilities in precision medicine, enabling clinicians to tailor treatments based on individual patient characteristics. This project focuses on developing a unified and efficient statistical framework to improve treatment decisions by leveraging rich demographic, socio-economic, and biomedical data. By advancing personalized decision-making, this research contributes to better health outcomes, more efficient healthcare delivery, and overall national well-being. The project also offers broad societal impact through its commitment to education, collaboration, and open science. The investigators will mentor graduate students and develop new coursework at the intersection of machine learning, statistics, and personalized medicine. In addition, all software tools developed will be released as open-source, supporting accessibility and reproducibility in scientific research. The interdisciplinary nature of the project encourages collaboration across statistics, medicine, and computer science, and prepares a next-generation workforce to tackle complex health data problems. This project aims to develop an efficient learning framework for estimating optimal individualized treatment rules (ITRs) across a broad range of personalized medicine settings. The proposed methodology is based on semiparametric modeling and is designed to address complex relationships among covariates, treatments, and outcomes