Public health data available for research are booming with the expansion of Big Data. This reshapes the data sources for DOHaD enquiries while offering ample opportunities to advance epidemiological modelling within the DOHaD framework. However, Big Data also raises a plethora of methodological challenges related to accurately characterising population health trajectories and biological mechanisms, within heterogeneous and dynamic sociodemographic contexts, and a fast-moving technological landscape. In this chapter, we explore the methodological challenges of research into the causal mechanisms of the transgenerational transfer of disease risks that characterise the DOHaD research landscape and consider these challenges in the light of novel technologies within artificial intelligence (AI) and Big Data. Such technologies could push further the collating of multidimensional data, including electronic health records and tissue banks, to offer new insights. While such methodological and technological innovations may drive clearer and reproducible evidence within DOHaD research, as we argue, many challenges remain, including data quality, interpretability, generalisability, and ethics.