Electrical Impedance Tomography (EIT) is a non-invasive imaging technique increasingly applied in biomedical diagnostics. Traditionally, EIT systems are stationary and require specialist knowledge to operate and interpret results. This study presents the development and evaluation of an innovative wearable EIT device designed for real-time monitoring of bladder function. The system enables visualization of bladder filling levels and is intended to support patients suffering from lower urinary tract dysfunctions. The device is designed for future integration with a mobile application that will inform users about urinary incontinence episodes and bladder status. A key component of the system is the implementation of an image reconstruction framework based on machine learning algorithms. Three models were evaluated—Decision Tree, Elastic Net, and a Neural Network (NNET)—with a focus on optimizing accuracy, computational efficiency, and energy consumption. The results demonstrate that the NNET model offers superior reconstruction quality and the lowest prediction time, making it the most suitable for wearable medical applications. The proposed solution is based on a process model incorporating an optimization procedure essential for device control and energy-efficient operation.