Cardiovascular diseases, particularly heart disease, remain a significant global health concern, emphasizing the need for accurate predictive models to enable timely intervention and personalized treatment. In recent years, the fusion of multiple data modalities through multimodal learning and the aggregation of diverse models using ensemble techniques have shown remarkable potential for improving predictive accuracy. This study presents an innovative approach that combines multimodal learning and ensemble techniques to enhance heart disease prediction. Multimodal learning integrates information from various sources, such as clinical data, medical images, and patient histories, to create a comprehensive representation of patient health. In this study, multimodal data, including structured clinical information and medical images, are jointly processed and learned using deep learning architectures. The fusion of these diverse data modalities captures complex relationships and improves the model's understanding of underlying cardiovascular conditions. Ensemble techniques further amplify predictive performance by aggregating predictions from multiple models. This study employs an ensemble of diverse models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Support Vector Machines (SVMs), each trained on different aspects of the multimodal data. The ensemble leverages the strengths of individual models, compensating for their weaknesses and producing more robust and accurate predictions. To validate the proposed approach, a comprehensive dataset comprising clinical data and medical images will be collected and preprocessed. The multimodal learning framework will be designed to jointly process and extract features from the different data modalities. Subsequently, the ensemble of models will be trained and fine-tuned using the multimodal features. The performance evaluation will encompass various metrics, including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Comparative analyses will be conducted against standalone models and traditional machine learning techniques. This research contributes to advancing medical diagnostics by showcasing the effectiveness of leveraging diverse data sources and combining ensemble techniques to improve risk assessment and early detection.