More than 30% of worldwide deaths involve diseases caused by cardiovascular and lifestyle factors (WHO, 2023) As awareness of early risk identification advances, accessible practical screening tools for use in primary care continue to be either very expensive, reliant on specialists or both. In this paper, we propose a Multi-Modal AI-Based Health Intelligence Framework with an explicit focus on two interrelated concepts encapsulated in the form of two specialized individual modules: Disease Risk Assessment (DRA) module and Lifestyle Analysis (LSA) module. After systematic preprocessing and class-balancing, the DRA module trains LR, SVM, and RF on the Cleveland Heart Disease dataset (303 patients). The LSA module takes user-reported behavioral behaviors — BMI, physical activity, sleep, dietary quality, and stress — to calculate a composite Lifestyle Risk Index (LRI). Both modules are provided through a Streamlit web application that provides real time predictions with SHAP-based explanation. Amongst all the classifiers we evaluated, Random Forest performed best with a 91.8% accuracy, AUC-ROC = 0.956 It powers a sub 60 ms response time for the system and is deployable in the cloud.