Elevated left ventricular end-diastolic pressure (LVEDP) is an important index for the prognostication of several cardiovascular conditions, and is most reliably measured using cardiac catheterisation. This invasive surgical procedure carries potential risk of complications, high costs, and limits accessibility. We investigated the use of a multimodal machine learning model for non-invasive classification of elevated LVEDP, using a combination of apical two- and four-chamber 2D echocardiographic views, a 3D kinematic mesh of the left ventricle, and routine clinical measurements. The model achieved more sensitive results compared to clinical guidelines (sensitivity = 0.43 vs 0.25), but with lower accuracy (0.65 vs 0.76), specificity (0.74 vs 0.92), positive (0.38 vs 0.50) and negative (0.78 vs 0.80) predictive values. Among the different multimodal input types, the clinical measurements yielded the most significant influence on LVEDP classification, with a moderate influence of the apical four- and two-chamber views and a minimal effect of the 3D kinematic mesh. Different combinations of input types were explored, which highlighted how the interactions between different inputs influenced predictions and the potential for selecting specific combinations based on clinical objectives. This approach may offer a better understanding of markers for elevated filling pressure and demonstrate the predictive value of multimodal input data.