This paper presents a smart, non-invasive blood pressure monitoring system that integrates advanced sensors with artificial intelligence (AI) for continuous and accurate health tracking. Unlike traditional cuff-based devices, the proposed system employs photoplethysmography (PPG), electrocardiography (ECG), and fiber optic ballistocardiography (FO-BCG) sensors to capture real-time physiological signals. Machine learning models such as Support Vector Regression (SVR), Random Forest (RF), and Neural Networks analyze these signals to estimate systolic and diastolic pressures with medical-grade precision. The system achieves a mean absolute error (MAE) of less than 5 mmHg, ensuring compliance with AAMI and ISO 81060-2 standards. IoT-enabled connectivity allows remote monitoring, cloud-based storage, and instant alerts, making the solution ideal for both clinical and home healthcare environments. Overall, this work demonstrates how AI-driven sensor fusion can transform blood pressure monitoring into a reliable, comfortable, and patient-friendly digital health solution.