The human innate immune system consists of several components, which include antimicrobial peptides, pattern-recognition receptors, cytokines, and immune cells. In this dissertation, we explore unifying themes underlying the antimicrobial, membrane remodeling, and immunomodulatory behaviors of antimicrobial peptides and related molecules, and their interactions with microbial and mammalian cells. We utilize machine learning on antimicrobial peptides to examine the physicochemical parameters characteristic of membrane curvature generation, and develop a search tool to discover hidden antimicrobial and membrane-remodeling activity in new and existing taxonomies of multifunctional peptides and proteins, including mitochondrial fission proteins, histones, and neuropeptides. Using structural characterization and calibrating immune cell stimulation experiments, we outline molecular rules for antimicrobial peptide-mediated immunomodulation via ligand clustering of nucleic acids. Antimicrobial peptides condense naked DNA, nucleosomal DNA, and dsRNA into nanocrystalline immunocomplexes, which drastically amplify inflammation via multivalent binding to Toll-like receptors in immune cells. This work has broad implications for the deterministic control of inflammation in the contexts of infection, chronic inflammation, and autoimmune disease.