A system for determining blood pressure dip in a subject via machine learning models, the system comprising: a wearable device configured to be worn by the subject, the wearable device comprising: a photoplethysmographic sensor configured to generate output signals conveying information related to a bloodflow of the subject; and a motion sensor configured to generate output signals conveying information related to motion of the subject; and one or more processors operatively coupled to the wearable device and configured by machine-readable instructions to: receive output PPG signals from the PPG sensor over a period of time; receive output motion signals from the motion sensor over the period of time; determine, based on the motion signals, a motion level of the subject for a portion of the motion signals corresponding in time to a portion of the PPG signals; responsive to the motion level being less than a predetermined motion level, determine, based on the portion of the PPG signals, one or more bloodflow features, the one or more bloodflow features indicating a variability of the bloodflow and/or a shape of a bloodflow curve over the period of time; determine, based on the PPG signals, a heart rate variability of the subject over the period of time; determine, based on the HRV, a plurality of HRV features, the HRV features indicating changing characteristics of the heart rate variability over the period of time; determine an entropy gradient of the subject utilizing the plurality of HRV features and the motion signals in an ongoing manner over the period of time; determine, based on the entropy gradient, a physical state of the subject, the physical state being an awake state when it is determined that the entropy gradient is decreasing, and the physical state being an asleep state when it is determined that the entropy gradient is increasing; provide the one or more bloodflow features and the physical state to a machine learning model; cause the machine learning model to determine the blood pressure dip of the subject for the period of time based on at least the one or more bloodflow features and the physical state; and cause transmission of the blood pressure dip to the wearable device. a wearable device configured to be worn by the subject, the wearable device comprising: a photoplethysmographic sensor configured to generate output signals conveying information related to a bloodflow of the subject; and a motion sensor configured to generate output signals conveying information related to motion of the subject; and one or more processors operatively coupled to the wearable device and configured by machine-readable instructions to: a photoplethysmographic sensor configured to generate output signals conveying information related to a bloodflow of the subject; and a motion sensor configured to generate output signals conveying information related to motion of the subject; and one or more processors operatively coupled to the wearable device and configured by machine-readable instructions to: receive output PPG signals from the PPG sensor over a period of time; receive output motion signals from the motion sensor over the period of time; determine, based on the motion signals, a motion level of the subject for a portion of the motion signals corresponding in time to a portion of the PPG signals; responsive to the motion level being less than a predetermined motion level, determine, based on the portion of the PPG signals, one or more bloodflow features, the one or more bloodflow features indicating a variability of the bloodflow and/or a shape of a bloodflow curve over the period of time; determine, based on the PPG signals, a heart rate variability of the subject over the period of time; determine, based on the HRV, a plurality of HRV features, the HRV features indicating changing characteristics of the heart rate variability over the period of time; determine an entropy gradient of the subject utilizing the plurality of HRV features and the motion signals in an ongoing manner over the period of time; determine, based on the entropy gradient, a physical state of the subject, the physical state being an awake state when it is determined that the entropy gradient is decreasing, and the physical state being an asleep state when it is determined that the entropy gradient is increasing; provide the one or more bloodflow features and the physical state to a machine learning model; cause the machine learning model to determine the blood pressure dip of the subject for the period of time based on at least the one or more bloodflow features and the physical state; and cause transmission of the blood pressure dip to the wearable device.