Abstract: Selecting the right clinical sites is one of the most critical drivers of trial success, yet current approaches rely heavily on expert judgment. This work presents an end-to-end analytics framework that integrates machine-learning prediction with dynamic optimization to improve clinical site selection at Takeda. Using over 14,000 historical site-study observations and ~140 study and site characteristics, we developed models that accurately predict the probability that a site will be non-enrolling, classify enrollment performance tiers, and estimate the time-to-enrollment inflection point. These predictions feed into a mixed-integer optimization engine that recommends the optimal subset of sites for a given study, balancing expected enrollment and operational constraints such as geography and cost. his talk will share the methodology, predictive drivers, optimization logic, and key learnings from early implementation.