This research advances continuous, non-invasive bladder volume monitoring in a wearable format using bioimpedance. Conventional bioimpedance analysis typically relies on desktop hardware and, in the case of electrical impedance tomography, requires computationally intensive reconstruction and estimation methods. The present studies establish a fundamental understanding and introduce methods for transitioning to an optimized wearable platform. Through digital twin modeling, a framework was developed to optimize electrode placement and measurement frame selection, thereby maximizing bladder sensitivity. These optimizations indicate that accurate volume estimation can be achieved with minimal computational cost, supporting direct implementation on wearable electronics. Additional modeling provides the first systematic characterization of the effects of ascitic fluid, waist size, and subcutaneous fat on bioimpedance-based volume monitoring. Furthermore, generalized bladder volume estimation algorithms were developed that can match or, in some cases, exceed the performance of patient-specific methods. By integrating electrode placement optimization, sensitivity analysis, characterization of key confounding variables, and algorithms suitable for edge computing, this research substantially advances wearable bioimpedance sensing technology.