Computed Tomography (CT) imaging faces limitations, including low spatial resolution and noise, particularly in low-radiation-dose imaging. To address these challenges, researchers are exploring CT image reconstruction from sinogram data. Sinograms represent X-ray absorption throughout the body, and sophisticated image reconstruction methods, including machine learning algorithms and generative adversarial networks (GANs), can improve precision and resolution without increasing patient radiation exposure. This study proposes an iterative reconstruction approach that combines filters from deep learning models (Convolutional Neural Networks and UNet) with the Maximum Likelihood Expectation Maximization (ML-EM) algorithm and the Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) model. Our method aims to enhance image quality and reconstruction speed. Experimental results show significant improvements in image quality and resolution, with the proposed method (DL-MLEM-IR-UNET-ESRGAN) achieving an average SSIM of 0.9980 and PSNR of 53.2119, outperforming other methods. Additionally, our method reduces reconstruction time, with an average runtime of 131 seconds.