In a concerted effort to enhance the integration of machine-learning with multi-omic data, health stakeholders are launching multi-center consortia to establish standardized protocols for methylation assays. This initiative follows a clear strategic trajectory toward deploying these advancements in clinical diagnostics and prognostics within the next 3–5 years. However, challenges such as data heterogeneity and regulatory hurdles pose significant risks to widespread adoption. Engaging regulators early and developing transparent ML pipelines will be crucial in navigating these complexities.