Magnetic Resonance Imaging is a widely used technique to obtain images of the interior of the human body for diagnosis and treatment. MRI machines capture the raw signal in spatial frequency domain i.e. k-space and the image is obtained via Fourier transform. The Cartesian acquisition is one of the most commonly used acquisition patterns in MRI and is most susceptible to the patient's motion. Due to long scanning times, the possibility of the patient's movement is higher which introduces bulk motion artifacts reducing the quality of the image. Motion artifacts can affect the diagnosis and the necessity of re-scanning can cause significant financial costs as well as delays in diagnostics. Current methods for correcting motion artifacts work in image domain which need completely sampled k-space for reconstruction and hence are not useful for real-time artifacts correction. In this thesis, machine learning methods that can detect, estimate and correct motion artifacts in k-space were investigated making it possible to correct artifacts in real-time without the necessity of reconstruction. For each of these methods, we analyze the performance and discuss the merits and demerits.