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Aims. The derivation of spectroscopic parameters for M dwarf stars is veryimportant in the fields of stellar and exoplanet characterization. The goal ofthis work is the creation of an automatic computational tool, able to derivequickly and reliably the T$_{\mathrm{eff}}$ and [Fe/H] of M dwarfs by usingtheir optical spectra, that can be obtained by different spectrographs withdifferent resolutions.  Methods. ODUSSEAS (Observing Dwarfs Using Stellar SpectroscopicEnergy-Absorption Shapes) is based on the measurement of the pseudo equivalentwidths for more than 4000 stellar absorption lines and on the use of themachine learning Python package "scikit-learn" for predicting the stellarparameters.  Results. We show that our tool is able to derive parameters accurately andwith high precision, having precision errors of ~30 K for T$_{\mathrm{eff}}$and ~0.04 dex for [Fe/H]. The results are consistent for spectra withresolutions between 48000 and 115000 and SNR above 20.

ODUSSEAS: A machine learning tool to derive effective temperature and metallicity for M dwarf stars