Introduction: While reliable  in vitro testing for sensitivity to common aeroallergens has been available for decades, if and how asthma might be predictably expressed in people matched for comparable multiple sensitizations is unknown.   Objective: Our aim is to develop an understanding of these relations, which are known as allergic poly-sensitizations (APS) using a machine learning approach. We performed an audit of adult urban patients with moderate to severe asthma who presented to an urban outpatient pulmonary clinic. Methods: We constructed a database of sensitizations to the 25 aeroallergens in the zone 1 ImmunoCAP® assay. We used the Scikit-Learn® machine learning library to perform model-based clustering to identify APS clusters. Subsequently, clusters were compared for differences in clinical markers of allergic asthma.  Results: The database consisted of 509 patients. Mixture modeling identified ten clusters of increasing APS of varying size (n = 1 to 339). There were significant increases in mean serum immunoglobulin E (p<.001), peripheral blood eosinophil count (p<.001), and D _LCO (p=.02) with increasing APS. There was a significant decline in mean age at presentation (p<.001), FEV ₁/FVC (p=.01), and FEF _25-75 (p=.002), but not FEV ₁ (p=.29), nor RV/TLC (p=.14) with increasing APS by simple linear regression. Finally, we identified two apparent divergent paths for the poly-atopic march, one driven by perennial allergens and the other by seasonal allergens.  Conclusion: We provide the framework for a novel machine learning understanding and approach to the classification of APS and its heretofore under-appreciated potential influences on asthma cluster analyses. To our knowledge, this represents the first attempt to identify poly-sensitization patterns that have clinical implications.