The growing interest in battery-operated electric vehicles and renewable electricity storage, as well as increased use of portable devices, has created the need for “smart” or predictive battery management systems. Presently, battery management systems (BMS) have limited functionality and only allow for the study of batteries through basic monitoring which protects the battery from exceeding its limits and alerts about faults but lacks predictive capability, battery health monitoring, and optimization for the specific operating environment. In this paper, the proposed design consists of the use of an ESP32 microcontroller which will continuously read battery voltage, current, and temperature parameters, while the time-stamped values will be synchronized in real-time down to microsecond precision with the accessible cloud Digital Twin (DT) of the opposite BMS. In addition to the BMS readings, the Digital Twin will help to provide the amount of AI-assisted data analysis of all of the data collected in an effort to improve estimated State of Charge (SOC), State of Health (SOH) values, and prediction of degradation models. The designed IBMS as proposed is an eco- friendly and cost-effective electric mobility and smart grid systems battery. The new design brings watershed in precision, efficiency, dependency as well as total performance.