Yttrium-90 (90Y) radioembolization is a liver cancer therapy based on 90Y microspheres injected into the hepatic artery. Current dosimetry methods used to estimate the absorbed dose in order to prescribe the 90Y activity to inject are not accurate, which can affect the treatment effectiveness. A new dosimetry based on the hemodynamics simulation of the hepatic arterial tree, CFDose, aimed at overcoming some of the limitations of the current methods. However, due to the expensive computational cost of computational fluid dynamics (CFD) simulations, this method needs to be accelerated before it can be used in real-time during treatment planning. In this paper, we introduce a convolutional neural network model trained with the CFD results of a patient with hepatocellular carcinoma to predict the 90Y distribution under different downstream vasculature resistance conditions. The model performance was evaluated using two metrics, the mean squared error and prediction accuracy. The prediction accuracy showed that the average difference between the actual and predicted data was less than 1%. The proposed model could estimate the 90Y distribution significantly faster than a CFD simulation.