Precision medicine tailors treatments to patients' genetic profiles, thereby revolutionizing care and significantly improving outcomes. However, it has also driven healthcare costs to unsustainable levels, threatening equitable healthcare access. While advancements in biotechnology and genomics paved the way for precision medicine, critical gaps due to traditional clinical decision-making and payment schemes hinder its cost-effective adoption. I propose an interdisciplinary framework integrating Bayesian learning, stochastic optimization, and game theory, to optimize precision medicine ecosystems from the perspectives of clinicians, industry, and policymakers. This research will develop novel optimization models for personalized clinical decision-making and novel payment schemes, ensuring affordable precision medicine.