In this article, we present a novel approach for robust state estimation in linear uncertain descriptor systems, with a specific focus on the cardiovascular system (CVS) as a case study. Descriptor systems, which extend traditional state-space representations by incorporating algebraic constraints, present significant challenges in observer design, especially under conditions of uncertainty. To address these challenges, we propose the design of a functional observer using the
H
∞
{H}_{\infty }
filter approach, capable of managing uncertainties arising from both external disturbances and variations in system parameters. The observer is designed using a unique linear matrix inequality framework, which ensures the stability and performance of the estimation error system. The convergence of the estimation error is rigorously analyzed using the Lyapunov method. The effectiveness of the proposed method is demonstrated through simulations on a CVS model, highlighting its potential to reliably estimate unknown states in the presence of uncertainties.