In reservoir engineering, history matching plays a crucial role in calibrating simulation models to observed field data. Nonetheless, history matching is an ill-posed problem that contains high dimensional parameters, which deters efficient and fast predictions of reservoir behavior. This study introduces a unique approach of integrating Variational Autoencoders (VAE) in history matching to reduce the dimensions of its parameters. VAE is a powerful tool that generates low-dimensional representative parameters from the original realizations. Ensemble smoother with multiple data assimilation (ESMDA) is used to incorporate VAE into history matching. The encoded parameters were assimilated, and production parameters were generated throughout. Then, after the final iteration, the realizations were decoded. For the sake of practicality, only permeability was used as a history matching parameter in this study, besides 20000 realizations of synthetic channelized reservoirs. Several sensitive criteria were tested, such as the number of training epochs and changing the values of the inflation factor, 𝛼. The total running time was 6.5 hours to train the VAE model and to run an ensemble of 50 realizations of permeability realizations in ES-MDA. The results indicate a promising approach to significantly reduce the dimensionality of the parameters from 45*45 grid cells to a 1-dimensinoal array of 200 values only, while keeping geologic realism. All is achieved by using computationally effective and accurate means, which highlights the potential of VAE in enhancing history matching processes to be used in various reservoir engineering applications.