Atrial fibrillation and ventricular diastolic dysfunction (DD) are highly prevalent and frequently coexisting cardiovascular disorders. Their interplay promotes atrial remodeling and enlargement, exacerbating left atrium (LA) hemodynamic and mechanical dysfunction, increasing thromboembolic risk. Existing hemodynamic models typically rely on static geometries, neglecting dynamic cardiac cycle variations and the effects of closed mitral valve. Furthermore, 3D atrial hemodynamics in DD remain poorly characterized, highlighting the need of research to improve risk stratification and therapeutic strategies. We propose a novel and streamlined computational approach integrating dynamic LA volume changes - derived from clinically obtained phasic volume data - into fluid dynamic simulations. This method enables a comprehensive evaluation of LA mechanical performance and high-resolution 3D hemodynamics, including thrombogenic risk assessment, across diverse clinical scenarios. A single 3D LA model, acquired at end-systole, served as the geometric basis for generating N volume-varying meshes spanning the entire cardiac cycle during pre-processing. The meshes were dynamically changed in the 3D hemodynamic model, according to reservoir, conduit and booster pump phases, also accounting for the closed valve effects. Three scenarios were evaluated: (1) a healthy case, (2) pseudonormal DD, (3) pseudonormal DD with a rigid mesh. Static (rigid-wall) simulations could lead to overestimation of blood stasis zones, also prediction higher transmitral velocity even in the reservoir phase (closed valve period). In contrast, the dynamic approach predicted the blood flow deceleration in reservoir phase, while the LA volume increased to its maximum, providing a more realistic hemodynamic pattern. The method bridges a gap in atrial hemodynamic modeling with a clinically feasible computational approach. It enables personalized cardiovascular risk assessment without costly simulations or dynamic LA reconstruction from complex medical imaging. Pathophysiological scenarios can be modeled using simple clinical data, e.g. ultrasound assessment of patient-specific volume and flow profiles.