We present zELDA (redshift Estimator for Line profiles of Distant Lyman-Alpha emitters), an open source code to fit Lyman-α (Lyα) line profiles. The main motivation is to provide the community with an easy to use and fast tool to analyze Lyα line profiles uniformly to improve the understating of Lyα emitting galaxies. zELDA is based on line profiles of the commonly used ‘shell-model’ pre-computed with the full Monte Carlo radiative transfer code LyaRT. Via interpolation between these spectra and the addition of noise, we assemble a suite of realistic Lyα spectra which we use to train a deep neural network.We show that the neural network can predict the model parameters to high accuracy (e.g., ≲ 0.34 dex HI column density for R ∼ 12000) and thus allows for a significant speedup over existing fitting methods.As a proof of concept, we demonstrate the potential of zELDA by fitting 97 observed Lyα line profiles from the LASD data base. Comparing the fitted value with the measured systemic redshift of these sources, we find that Lyα determines their rest frame Lyα wavelength with a remarkable good accuracy of ∼0.3Å (∼75 km/s).Comparing the predicted outflow properties and the observed Lyα luminosity and equivalent width, we find several possible trends. For example, we find an anticorrelation between the Lyα luminosity and the outflow neutral hydrogen column density, which might be explained by the radiative transfer process within galaxies.