Smart, ultra-scaled, always-on wearable, and implantable (WI) sensors are an exciting frontier in personalized medicine. These sensors integrate sensing and actuation capabilities, enabling real-time analyte detection for on-demand drug delivery, akin to a biological organ. The microneedle (MN)-based patch serves as a critical novel interface element in this system. It is inexpensive, minimally invasive, and safe, showing promise in glycemic management and insulin therapy in laboratory and animal studies. However, the current design of MNs relies primarily on empirical approaches, with significant challenges. These challenges include potential diffusion delays that may impede time-critical drug intervention and an iterative design process lacking a clear understanding of the trade-off between response time and limits of detection. In this paper, we introduce the first predictive framework for MN sensors, based on physical scaling laws and biomimetic concepts. Our framework is supported by experimental and numerical validations, establishing analytical scaling relationships that capture the fundamental workings of hollow and porous-swellable MN sensors. It quantifies essential performance metrics like "response time (RT)" and "limit of detection (LOD)" while assessing trade-offs associated with various geometrical and physical parameters of the MN technology. As a result, our model provides a universal framework for interpreting/integrating experimental findings reported by laboratories worldwide. By leveraging this predictive framework, researchers can advance the development and optimization of MN sensors, leading to improved performance and expanded applications in the field of wearable and implantable technologies.