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The assessment of oral carbohydrate intake and its rate of exogenous glucose appearance is crucial for monitoring blood glucose in patients who suffer from diabetes and also for healthy individuals, as it is one of the major factors involved in human metabolism. Its accurate modelling is necessary when developing methodologies to mimic the physiological processes within the human body. Considering the recent advancements in data-driven methods that demand non-deterministic solutions to simulate real-life scenarios, this study proposes a novel approach based on conditional generative adversarial models to introduce realistic variability to the models in the state of the art, which are incapable of representing the full variety of scenarios due to their deterministic nature.