Cine cardiac magnetic resonance (cine-CMR) imaging is the gold-standard modality for assessing myocardial structure and function, including Left Ventricular Ejection Fraction (LVEF) measurement. However, cine-CMR presents challenges due to its high-dimensional 3D + time nature. To address this, we propose cine-CLIP, which employs standard deviation (std) mapping across time to reduce 4D data to 3D while preserving dynamic and spatial information. Through extensive experiments on publicly available UK Biobank and ACDC datasets, our method, cine-CLIP, achieves state-of-the-art (SOTA) LVEF prediction with a mean absolute error (MAE) of 2.523, outperforming other techniques. To assess generalizability, we further validate our method on the external Kaggle Data Science Bowl dataset, which follows a slightly different CMR acquisition protocol. Despite domain shifts, we achieved an MAE of 5.091, surpassing prior methods. These findings highlight Cine-CLIP’s ability to capture the high-dimensional complexity of cardiovascular disease from cine-CMR data. LVEF prediction serves as a proof of concept, demonstrating the model’s effectiveness in this task. However, this framework has the potential to be extended to other clinical metrics. The code is available at https://github.com/enriquealmar9/Cine-CLIP .