This paper studies how an edge recurrent neural network can improve a Battery Monitoring System (BMS). The proposed monitoring measures current, voltage, and temperature and infers the State of Charge (SoC) and State of Health (SoH) values through machine learning in an embedded system. The study relies on two test cases: a theoretical one using NASA’s battery dataset, where the high volume of data is best suited for a study, and a second one with a system built in the University of São Paulo where this paper can analyze more profound the practical results of its use. This system of the second test case consists of peripheral sensors integrated into an Internet of Things (IoT) platform, sending the data collected from a VRLA battery to a Single Board Computer (SBC) via Bluetooth Low Energy (BLE). The edge SBC concentrates this received data - from one or more IoT nodes - generating new data for supervision and enabling control. The SBC communicates with a Web server in a one-way route to send the battery data without any data request. The algorithm developed for the SoC uses a dense recursive network with Mean Absolute Error (MAE) up to 0.2, and for SoC above 10%, the Mean Absolute Percentage Error (MAPE) can reach 0.16%. As an SoH calculation method, the battery replacement performs the methodology when the capacity reaches 80% of its nominal capacity. It is essential to highlight that these results are from a devices with limited hardware availability without cloud communication.