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Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide. Early prediction and timely intervention are critical to reducing the burden of heart disease. This study proposes SmartHeart, a conceptual framework that integrates structured clinical data with a proposed real-time data acquisition pipeline for interpretable cardiovascular risk prediction. A publicly available heart disease dataset - aggregated from multiple clinical sources and shared in a merged, cleaned form on Kaggle, containing 11 clinical variables and 1190 patient records, was used to train and evaluate six supervised machine learning models: Support Vector Classifier (SVC), Random Forest, XGBoost, CatBoost, AdaBoost, and Extra Trees Classifier. Following rigorous preprocessing, model performance was assessed using a stratified nested 5-fold cross-validation framework, where an inner loop optimized hyperparameters and an outer loop provided robust internal performance estimation, followed by final evaluation on an independent held-out test set. Among all models, Random Forest achieved the highest performance, with an accuracy of 92.86 % and an AUC of 97.14 %, supported by 95 % confidence intervals and pairwise t-tests confirming its statistical superiority. To enhance interpretability, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) were applied to explain individual predictions, identifying features such as chest pain type, ST slope, and maximum heart rate as key contributors. While the real-time component remains at the architectural and conceptual stage, the proposed SmartHeart framework lays the foundation for future integration into cloud-based healthcare systems, enabling explainable and proactive cardiovascular risk assessment.