Disease prediction remains a critical area of focus in health care, demanding high accuracy and efficiency in identifying potential health risks. Traditional classification algorithms, while effective in many scenarios, often face limitations when processing complex, high-dimensional medical data. This study proposes a novel hybrid model that combines deep learning architectures, specifically convolutional neural networks (CNNs), with traditional classification algorithms such as support vector machines (SVMs) and random forests (RFs), to enhance disease prediction accuracy and robustness. Leveraging the feature extraction capabilities of CNNs, the hybrid model integrates these features with the superior classification capabilities of traditional algorithms to optimize performance. The study employs an optimized pipeline that includes data preprocessing, feature selection, and hyperparameter tuning to further refine model outcomes. These experiments, conducted on publicly available medical datasets, reveal that the proposed hybrid model outperforms standalone models in terms of accuracy, precision, recall, and F1-score. Additionally, an ablation study demonstrates the contribution of each model component to overall predictive power. This approach highlights the potential of hybrid deep learning models as powerful tools for early disease detection, providing healthcare practitioners with reliable and efficient diagnostic support. Future work aims to explore the scalability of this approach across various disease datasets and expand its applications to real time.