Spinal cord injuries (SCI) have vast effects on day-to-day life, including the loss of sensation and control of the bladder. Since elevated bladder pressures from urine production and storage can be detrimental to renal functionality, urologists recommend performing clean-intermittent catheterization (CIC) every two to four hours throughout the day. However, limitations in mobility make the high frequency of these trips to the bathroom prohibitive. Sometimes a patient׳s bladder fills to capacity before performing CIC and eventually leaks urine, causing unnecessary embarrassment. As such, continence is the primary concern of most SCI patients. The issue in performing CIC is that it is a time-based approach, whereas urine production does not occur at a constant rate. A demand-based ’bladder almost-full’ warning system would provide more useful notifications and help SCI patients plan their bathroom trips accordingly. In this work, we explore using near-infrared light to create a discrete, wearable, non-invasive bladder state estimation system using machine learning to determine urine volume in the bladder to provide continuous monitoring for a patient throughout the day. We do this through proof-of-concept evaluation studies using Monte Carlo simulations and describe our bladder state estimation system. We also highlight some preliminary results using the system and distinguish differences in light intensity between a full and empty bladder on a volunteer.