Healthcare systems worldwide are under growing financial pressure as rising service demand, constrained budgets, and regulatory complexity challenge the sustainability of care delivery. Effective financial management has therefore become a strategic imperative, requiring healthcare organizations to move beyond retrospective accounting toward data-driven, analytical decision-making. From a broad perspective, healthcare financial management analytics integrates cost accounting, reimbursement modeling, and operational data to support transparency, efficiency, and value-based care objectives. By leveraging large volumes of clinical, administrative, and financial data, analytics enables managers to understand how resources are consumed, how revenues are generated, and where inefficiencies emerge across care pathways. This study narrows its focus to the evaluation of cost structures, reimbursement efficiency, and resource allocation under real-world operational constraints. It examines how analytical methods can be used to disaggregate fixed and variable costs, assess alignment between service costs and reimbursement mechanisms, and identify mismatches that erode financial performance. Particular attention is given to constraints such as capacity limits, workforce availability, and regulatory requirements that shape feasible allocation decisions. By framing financial management as an optimization problem informed by analytics, the paper highlights pathways for improving reimbursement capture, reducing waste, and supporting equitable resource distribution without compromising quality of care. The findings underscore the role of financial analytics as a critical enabler of sustainable, constraint-aware healthcare management.