[18MF]fluorodeoxyglucose (FDG) positron emission tomography (PET) aids in the localisation of the epileptogenic zone in patients with focal epilepsy, especially when magnetic resonance imaging (MRI) is normal or non-contributory. We propose a two-stage deep learning framework to support the clinical evaluation of patients with focal epilepsy by identifying candidate regions of hypometabolism in [18F]FDG PET scans. In the first stage, we train a generative adversarial network (GAN) to learn the mapping between healthy [18F]FDG PET and T1-weighted (T1w) MRI data. In the second stage, we synthesise pseudo-normal PET images from T1w MRI scans of patients with epilepsy to compare to the real PET scans. Comparing the estimated pseudo-PET images to the true PET scans in healthy control data, our GAN produced whole-brain mean absolute errors of 0.053±0.015, outperforming a U-Net (0.058±0.021) and a high-resolution dilated convolutional neural network (0.060±0.024; all images scaled 0–1). In a sample of 20 epilepsy patients, we created Z-statistic images (with thresholding at +2.33) by subtracting the patient’s true PET scans from their estimated pseudo-normal PET images to identify regions of hypometabolism. Excellent sensitivity for lobar location of abnormalities (92.9±13.1) was observed for the seven cases with MR-visible epileptogenic lesions. For the 13 cases with non-contributory MR, a lower sensitivity of 74.8±32.3 was observed. Our method performed better than a statistical parametric mapping analysis. Our results highlight the potential of deep learning-based pseudo-normal [18F]FDG PET synthesis to contribute to the management of epilepsy.