The influence of microbes on human health and disease is far-reaching. Multiple studies have demonstrated that the human microbiome influences performance and response to challenges, and with the continuous decrease in sequencing cost, we anticipate more and more studies aimed at connecting human microbiome dynamics to clinical outcomes. How we appropriately build and analyze these human-derived data sets to link microbiome features to host outcomes requires rigorous computational techniques that go beyond traditional statistical methods and, at the same time, allow us to retain biological interpretations that inform follow-up mechanistic investigations. Here, I present three disease-specific contexts where tailored machine learning pipelines were adapted and applied to multi-modal clinical microbiome datasets, which allowed the reveal of actionable and biologically meaningful insights. First, I demonstrate that anti-inflammatory commensals in the oropharyngeal cavity are predictive of not developing the need for respiratory support, providing early insights into host-microbe interactions during the COVID-19 pandemic. Next, I investigate the fecal microbiome of trauma-exposed patients and discover a novel possible microbiome-based mechanism influencing key blood-based biomarkers of PTSD. Finally, I characterize the microbiota dynamics of two older adult populations, nursing home residing and community-dwelling. Focusing on the potential contribution of the microbiome to Alzheimer’s disease(AD)-specific cognitive outcomes, we identify an association between worse cognitive outcomes and decreasing anti-inflammatory microbes and increased microbial encoded metabolic pathways with known AD association. The work presented here underscores the importance of integrating robust computational techniques to distill relevant information from clinical microbiome data to possible pathways by which this community impacts health and disease.