State of Charge (SoC) is an essential indicator for energy storage distribution in lithium-ion batteries, which prevents overcharge and over-discharge of the battery by integrating it into the Battery Management System. Common estimation approaches are Extended Kalman Filtering (EKF), Coulomb counting, open-circuit-voltage (OCV), and neural networks (NN). While these methods are able to estimate SoC more accurately, they lack real-time monitoring capability for internal and external battery damage such as air bubbles and foreign objects. This study is the first to propose a novel joint algorithm for real-time monitoring of battery SoC and health with high accuracy, integrating ultrasonic non-destructive testing (NDT). The proposed algorithm predicts the battery overcharge based on the ultrasonic signal and reveals structural changes in commercial batteries during charging/discharging. Experimental results demonstrate that ultrasonic inspection cannot only enhance the SoC estimation accuracy of the backpropagation (BP) neural network, but also detects damaged conditions of the battery.