The high-squint synthetic aperture radar (SAR) echo signal is characterized by serious coupling between azimuth di-rection and range direction, large range migration. Imaging with conventional range Doppler (RD) algorithm causes problems such as azimuth defocus and space variation. In order to solve the problems in the imaging process of high-squint SAR and imaging quality and calculation cost, a Learnable Range Doppler (LRD) imaging method for high-squint SAR based on deep unfolded network is proposed. This method combines RD algorithm with deep learning by using RD imaging depth steps to build an RD imaging study network structure. Taking the echo data as network input is to learn the imaging process from echo data to SAR image. Firstly, the learnable parameters of imaging network are determined based on the analysis of the echo signal model of high-squint SAR. Secondly, the SAR imaging network is designed according to the imaging process. Finally, the network is trained through unsupervised training method and output the learning imaging result. The simulation results of point targets and real scene targets show that the proposed method can effectively suppress side lobes, improve imaging accuracy and calculation efficiency, and meet the requirements of high-squint SAR imaging.