The integration of many imaging modalities, including MRI and X-ray, holds great promise for improving patient care and diagnostic accuracy in the quickly developing field of medical imaging. This chapter introduces a novel method for doing thorough analyses of multi-modal imaging data by combining convolutional neural networks (CNNs) with recurrent neural networks (RNNs). The suggested methodology uses RNNs to capture contextual linkages and temporal dependencies across sequential image datasets, while using CNNs’ strengths in spatial feature extraction from X-ray and MRI images. Our approach entails creating a hybrid deep learning model that uses CNNs to extract complex features from the images first, and then integrates these features into an RNN architecture to examine relationships and trends over time. This two-stage approach allows for a more holistic evaluation of imaging data, enabling improved detection of abnormalities, disease progression analysis, and treatment planning. We demonstrate our model’s superior accuracy and robustness over conventional single-modal analysis techniques by validating it on a variety of datasets that include both X-ray and MRI scans. The results demonstrate that our multi-modal framework not only enhances diagnostic performance but also provides valuable insights into the interdependencies between different imaging modalities. This study lays the groundwork for future advancements in medical imaging technologies by highlighting the potential of AI-driven multi-modal imaging analysis to transform clinical processes and enhance patient outcomes through quicker and more precise diagnosis.