Asthma is among the most common chronic diseases worldwide and is a significant contributor to the global health burden, highlighting the urgent need for primary prevention. This article outlines several practical and conceptual challenges that accompany primary prevention efforts. It advocates for improved predictive modeling to identify those at high-risk of developing asthma using automated algorithms within electronic medical records systems and explanatory modeling to refine understanding of causal pathways. Understanding the many issues that are likely to affect the success of primary prevention efforts helps the community of individuals invested in asthma prevention organize efforts and maximize their impact.