The ongoing transition toward value-based care in the United States health sector has intensified the demand for advanced, data-driven decision-making systems. In this evolving landscape, the strategic implementation of predictive analytics and business intelligence (BI) tools has emerged as a critical enabler for optimizing clinical, operational, and financial performance. This paper explores how U.S. healthcare organizations are deploying these technologies to anticipate patient needs, improve care coordination, and enhance resource allocation under increasingly complex reimbursement models. From a broad systems-level perspective, predictive analytics enables real-time forecasting of patient volumes, risk stratification, and early identification of high-cost or high-need populations. When integrated with BI dashboards, these forecasts empower stakeholders to visualize key performance indicators (KPIs), monitor financial trends, and guide strategic planning with precision. Furthermore, the convergence of clinical and financial datasets allows health systems to align operational workflows with value-based metrics such as readmission rates, episode cost, and care quality scores. This paper narrows its focus by examining case studies from U.S. hospitals and accountable care organizations (ACOs) that have successfully implemented predictive-BI frameworks. It evaluates critical success factors including data governance, cross-functional leadership, workforce training, and technology scalability. Additionally, it explores barriers such as algorithmic bias, interoperability gaps, and ethical concerns around patient-level financial modeling. Ultimately, the article offers a roadmap for healthcare executives seeking to transform enterprise performance using analytics as a strategic lever—demonstrating how predictive-BI ecosystems can deliver measurable improvements in population health outcomes, financial sustainability, and operational efficiency within the U.S. health sector.