Uroflowmetry is a non-invasive diagnostic test used to evaluate the function of the urinary tract. Despite its benefits, it has two main limitations: high intra-subject variability of flow parameters and the requirement for patients to urinate on demand. To overcome these limitations, we have developed a low-cost ultrasonic platform that utilizes machine learning (ML) models to automatically detect and record natural in-home voiding events, without any need for user intervention. This platform operates outside of human-audible frequencies, providing privacy-preserving, automatic uroflowmetries that can be conducted at home as part of daily routines. After evaluating several machine learning algorithms, we found that the Multi-layer Perceptron classifier performed exceptionally well, with a classification accuracy of 97.8% and a low false negative rate of 1.2%. Furthermore, even on lightweight SVM models, performance remains robust. Our results also showed that the voiding flow envelope, helpful for diagnosing underlying pathologies, remains intact even when using only inaudible frequencies.Clinical relevance- This classification task has the potential to be part of an essential toolkit for urology telemedicine. It is especially useful in areas that lack proper medical infrastructure but still host ubiquitous embedded privacy-preserving audio capture devices with Edge AI capabilities.