The purpose of this study is to investigate feasibility of estimating the specific absorption rate (SAR) in MRI in real time. To this goal, SAR maps are predicted from 3T- and 7T-simulated magnetic resonance (MR) images in 10 realistic human body models via a convolutional neural network. Two-dimensional (2-D) U-Net architectures with varying contraction layers and different convolutional filters were designed to estimate the SAR distribution in realistic body models. Sim4Life (ZMT, Switzerland) was used to create simulated anatomical images and SAR maps at 3T and 7T imaging frequencies for Duke, Ella, Charlie, and Pregnant Women (at 3, 7, and 9 month gestational stages) body models. Mean squared error (MSE) was used as the cost function and the structural similarity index (SSIM) was reported. A 2-D U-Net with 4 contracting (and 4 expanding) layers and 64 convolutional filters at the initial stage showed the best compromise to estimate SAR distributions. Adam optimizer outperformed stochastic gradient descent (SGD) for all cases with an average SSIM of <inline-formula> <tex-math notation="LaTeX">90.5 \mp 3.6 </tex-math></inline-formula> % and an average MSE of <inline-formula> <tex-math notation="LaTeX">0.7 \mp 0.6 </tex-math></inline-formula>% for head images at 7T, and an SSIM of ><inline-formula> <tex-math notation="LaTeX">85.1 \mp 6.2 </tex-math></inline-formula> % and an MSE of <inline-formula> <tex-math notation="LaTeX">0.4 \mp 0.4 </tex-math></inline-formula>% for 3T body imaging. Algorithms estimated the SAR maps for <inline-formula> <tex-math notation="LaTeX">224\times 224 </tex-math></inline-formula> slices under 30 ms. The proposed methodology shows promise to predict real-time SAR in clinical imaging settings without using extra mapping techniques or patient-specific calibrations.