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We utilize SuperCam's Mars microphone to provide information on wind speed and turbulence at high frequencies on Mars. To do so, we first demonstrate the sensitivity of the microphone signal level to wind speed, yielding a power law dependence. We then show the relationship between the microphone signal level and pressure, air and ground temperatures. A calibration function is constructed using Gaussian process regression (a machine learning technique) taking the microphone signal and air temperature as inputs to produce an estimate of the wind speed. This provides a high rate wind speed estimate on Mars, with a sample every 0.01 s. As a result, we determine the fast fluctuations of the wind at Jezero crater which highlights the nature of wind gusts over the Martian day. To analyze the turbulent behavior of this wind speed estimate, we calculate its normalized standard deviation, known as gustiness. To characterize the behavior of this high frequency turbulent intensity at Jezero crater, correlations are shown between the evaluated gustiness statistic and pressure drop rates/sizes, temperature and energy fluxes. This has implications for future atmospheric models on Mars, taking into account turbulence at the finest scales. Plain Language Summary: The NASA Perseverance mission sent microphones to the surface of Mars. This microphone has recorded signals due to the wind. We examine how these recorded signals vary with other sensor data from Perseverance, which shows a link between the microphone signal, the dedicated wind speed sensor and air temperature. Based on this finding, we develop a way to predict the wind speed from the microphone data using a machine learning technique. The microphone records data at a very high rate compared with sensors so far sent to Mars. This means that the wind speed predicted from the microphone data can be used to study the chaotic and variable wind behavior on Mars to a level never seen before. We show that this chaotic and variable behavior has links to temperature and the number of whirlwinds observed. This will lead us to better weather models for Mars. Key Points: Wind‐induced noise is observed by the SuperCam Mars microphone on PerseveranceMicrophone and air temperature data are used to estimate the wind speed at high frequencies, using a machine learning modelThe wind speed estimate is used to examine the relationships between turbulent intensity, pressure drops, temperature, and energy flux [ABSTRACT FROM AUTHOR]