The co-morbidity of cardiovascular disease (CVD) and pulmonary disease poses severe challenges in early detection and effective clinical management, as conventional diagnostic processes tend to diagnose these in isolation. This article advances a theoretical model for a single system of diagnosis that utilizes multimodal data—such as chest X-rays, computed tomography (CT), and electrocardiogram (ECG) signals—fused through sophisticated artificial intelligence methods. In particular, we propose a multi-modal machine learning structure based on the integration of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Support Vector Machines (SVM) to support concurrent detection and classification of CVD and respiratory disorders. The approach overcomes some major challenges like heterogeneous data pre-processing, feature combination, and model fusion. Although this paper is theoretical, it lays the groundwork for future empirical studies, such as the deployment of risk stratification models and clinical implementation. The framework will facilitate early intervention, eliminate duplicate diagnostic efforts, and improve patient outcomes through precision diagnostics powered by AI.