This research presents the design and implementation of a predictive analytics dashboard for short‑term capacity management in the UAC Program. The dashboard integrates statistical and machine‑learning models to forecast care load, admissions, and releases, while visualizing confidence intervals and capacity stress indicators. By combining technical accuracy with stakeholder‑friendly visualizations, the study demonstrates how predictive analytics can be transformed into actionable insights that support proactive planning, resource allocation, and risk‑aware decision‑making.