In this work, we present a deep-learning architecture to augment the sensing capabilities of a micro electro mechanical system (MEMS) for fluid mechanical applications. The MEMS sensor is composed of a polyvinylidene fluoride flexible piezoelectric flag and a bluff body, converting three-dimensional fluid mechanical energy flow into a timedependent voltage signals. The developed deep learning method allows for extracting accurate wind speed and classifying turbulences. The bluff body generates vortexes which are not only the functions of the bluff body’s geometry but also related to the fluid speed. These vortexes induce a mechanical vibration into the attached piezoelectric flag and hence generate charge displacement and an electrical voltage signal. By placing the mentioned setup in a wind tunnel, we excited the structure with various wind speeds while different combinations and geometries of the bluff body and piezoelectric flag were considered. An unsupervised autoencoder was used to extract the continuous manifold in Fourier spectra of time domain voltage generated by the piezoelectric sensor when the wind speed is continuously changed from 0 to 33 meters per second. We found that this manifold is highly correlated with wind speed. By adding another feed-forward network in parallel to the decoder network of the autoencoder we also incorporated the measured wind speed as the data’s label and could successfully use the system to extract the wind speed, despite the sensor was placed under the strong turbulence generated by the bluff body. We also investigated the ability of our deep learning method to classify different bluff bodies from the voltage harvested from the piezoelectric flag, finding that this unique cability is resilient to the wind speed in the range. Such a system can be turned into a system that fingerprints different turbulence and uniquely differs them for various applications.