Cardiovascular diseases (CVD) are among the leading causes of mortality worldwide, necessitating accurate and reliable predictive models to improve clinical decision-making. Existing approaches often focus on single-modality data, such as textual electronic health records (EHR) or visual electrocardiogram (ECG) signals, limiting their ability to capture complementary information across modalities. To address this, we propose the Multimodal Gated Fusion Framework (MGFF), a novel method that integrates textual EHR data and visual ECG signals through a gated fusion mechanism, leveraging the ViLT transformer for robust multimodal feature alignment and classification. The framework was evaluated on multimodal datasets, achieving an accuracy of 91.2%, an F1-score of 0.91, and a ROC-AUC of 0.94, significantly outperforming advanced baselines such as MedFuse and VisualBERT. Extensive experiments, including ablation studies and calibration analysis, demonstrated the importance of the gated fusion mechanism and the reliability of the predicted probabilities. While the results are promising, limitations such as robustness to noisy data and computational efficiency highlight areas for future improvement. The proposed MGFF provides a reliable, accurate, and scalable solution for CVD prediction, emphasizing the potential of multimodal approaches in advancing healthcare analytics. Future work will focus on enhancing robustness, generalizability, and interpretability for broader clinical adoption.