This proposal aims at advancing both methodologies and empirical insights in the field of consumer behaviour to support the transition to a sustainable and healthy food system. It integrates state-of-arts computational techniques to refine and innovate the research methods used in studying decision-making within the food sector, such as artificial intelligence and machine learning methodologies alongside the more traditional and established quantitative and qualitative research methods. In relation to empirical insights, consumer perception and behaviour towards topical issues such as climate change events will be analysed and modelled using appropriate algorithms, to identify how the different sections of the food system i.e. production, processing, consumption are being addressed by the public. These advancements will empower food chain actors to implement effective and data-driven strategies to improve public health and environmental sustainability.