<p dir="ltr">Ultra-scaled, always-on, smart, wearable and implantable (WI) therapeutic platforms define the research frontier of modern personalized medicine. The WI platform integrates real-time sensing with on-demand therapy and is ideally suited for real-time management of chronic diseases like diabetes. Traditional blood tracking methods, such as glucometers, are insufficient due to their once-in-a-while measurements and the imprecision of insulin injections, which can lead to severe complications. To address these challenges, researchers have been developing smart and minimally invasive microneedle (MN) components for pain-free glucose detection and drug delivery, potentially functioning as an "artificial pancreas". Inspired by natural body homeostasis, these platforms must be accurate and responsive for immediate corrective interventions. However, artificial MN patches often have slow readings due to factors like MN morphology and composition that remain poorly understood, hindering their optimization and integration into real-time monitoring devices. Despite extensive, iterative experimental efforts worldwide, a holistic framework incorporating the interaction between MN sensing and therapy with fluctuating natural body functions is missing. In this thesis, we propose a generalized framework for glycemic management based on the interaction between biological processes and MN-based operations. The results, incorporating theoretical insights from the 1960s and recent advancements in MN technology, are platform-agnostic. This generality offers a unique template to interpret experimental observations, justify the recent introduction of drugs like GLP-1 cocktails, and optimize platforms for accurate and fast disease management. </p>