A system for urination prediction, the system comprising: one or more data processors; and a non-transitory computer-readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform operations including: receiving biometric data associated with a unique identifier, wherein the received biometric data is heart rate data, ambient temperature data, body temperature data, blood oxygenation data, skin conductivity data, blood pressure data, electrocardiogram data, electroencephalogram data, electromyographic data, respiration rate data, sleep quality data, depth of sleep data, restfulness data, heart rate variability data, or combinations thereof; accessing a urination prediction model associated with the unique identifier; generating an updated urination prediction model using the received biometric data; predicting an expected urination time using the updated urination prediction model and the received biometric data; generating a status update using the expected urination time and the unique identifier, wherein generating the status update facilitates generation of a stimulus; and receiving a feedback signal associated with the unique identifier, wherein the feedback signal is indicative of an occurrence of a urination event, and wherein generating the updated urination prediction model includes applying a machine learning technique to the urination prediction model using the feedback signal and the received biometric data. one or more data processors; and a non-transitory computer-readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform operations including: receiving biometric data associated with a unique identifier, wherein the received biometric data is heart rate data, ambient temperature data, body temperature data, blood oxygenation data, skin conductivity data, blood pressure data, electrocardiogram data, electroencephalogram data, electromyographic data, respiration rate data, sleep quality data, depth of sleep data, restfulness data, heart rate variability data, or combinations thereof; accessing a urination prediction model associated with the unique identifier; generating an updated urination prediction model using the received biometric data; predicting an expected urination time using the updated urination prediction model and the received biometric data; generating a status update using the expected urination time and the unique identifier, wherein generating the status update facilitates generation of a stimulus; and receiving a feedback signal associated with the unique identifier, wherein the feedback signal is indicative of an occurrence of a urination event, and wherein generating the updated urination prediction model includes applying a machine learning technique to the urination prediction model using the feedback signal and the received biometric data. receiving biometric data associated with a unique identifier, wherein the received biometric data is heart rate data, ambient temperature data, body temperature data, blood oxygenation data, skin conductivity data, blood pressure data, electrocardiogram data, electroencephalogram data, electromyographic data, respiration rate data, sleep quality data, depth of sleep data, restfulness data, heart rate variability data, or combinations thereof; accessing a urination prediction model associated with the unique identifier; generating an updated urination prediction model using the received biometric data; predicting an expected urination time using the updated urination prediction model and the received biometric data; generating a status update using the expected urination time and the unique identifier, wherein generating the status update facilitates generation of a stimulus; and receiving a feedback signal associated with the unique identifier, wherein the feedback signal is indicative of an occurrence of a urination event, and wherein generating the updated urination prediction model includes applying a machine learning technique to the urination prediction model using the feedback signal and the received biometric data.