The cell and gene therapy field has seen a staggering increase in products on the market, with an expectation for continued growth in the coming years. However, widespread development of commercially viable products addressing more therapeutic indications and larger patient populations is hindered by the complexity of these products and the consequent high costs for their manufacturing. Innovations in upstream, downstream, and analytical tools that account for such complexity is urgently needed to reduce costs, improve quality and yield, and ultimately make these therapies more affordable. One barrier to cost-effective bioprocesses is the poor knowledge of the biology underlying product manufacturing. Poor analytical characterisation limits the generation of bespoke production systems and reliable in-process control strategies. To address this knowledge gap, we carried out an unprecedented multi-omics study to unravel the biology underpinning a scalable adeno-associated viral vector (AAV) process in transiently transfected HEK293 cell clones. We conducted a time-course analysis to investigate transcriptomics, translation, proteomics, and metabolomics while comparing high and low producer HEK293 clones. Our integration of these multi-omics methods has revealed unique molecular kinetics and enabled an initial identification of differential pathways and biomarkers that are enriched in the high AAV yield production but not for the low producer clone. Further to these results, we are now applying machine learning and artificial intelligence tools to identify key AAV production biomarkers and subsequently engineer them to enhance AAV productivity. Through harnessing the knowledge of the AAV production system biology in HEK293 cells, we are set out to generate novel production systems that may be fine-tuned by a rationally designed in-process control strategy. Productivity improvements for gene therapies, and particularly for AAVs, are key to reducing manufacturing costs and ultimately enabling patients’ access to advanced treatments for indications with large unmet therapeutic need.