The development of new antimicrobial drugs is becoming more urgent than ever due to the rapid emergence of antibiotic resistance and limitations in bacteria targets. A promising alternative that received considerable scientific attention is antimicrobial peptides (AMPs), also known as host defense peptides. In this work, we aim to facilitate the design of more effective peptides using computational tools by solving the following two main challenges in the field. First, the underlying microscopic mechanisms of how AMPs interact with bacteria and other pathogens remain inadequately understood. Second, the infinite possibilities in engineering new peptides is a time-consuming task. We developed a theoretical framework for the interactions of AMPs and bacteria on the single-cell and population levels. We also investigated the effect of AMP cooperativity on efficacy as measured by minimal inhibitory concentrations (MIC), fractional inhibitory concentrations (FIC), and our acceleration parameter R by looking at cases with 1, 2, 3, and eventually an arbitrary number m types of AMPs. Our results explained the broad concentration spectrum where different types of AMP operate more optimally, offering a mechanistic explanation of the bacterial clearance dynamics and AMP cooperativity mechanisms. Increasing the number of AMP components in a mixture while keeping the total amount fixed enhances their synergistic activities, and strong cooperativity can be achieved for weak intermolecular interactions, providing a qualitative measure for the degree of cooperativity applicable in natural systems. We also used feature selection methods to build our machine learning pipeline to extract features that make peptides antimicrobial. This model produced decent accuracy with manual hyperparameter tuning, and the results can be applied to engineer better AMPs.