Wireless Body Area Network (WBAN) technologies play a crucial role in the digitalization of healthcare. These systems enable real-time monitoring of individuals’ vital signs via wearable or implantable sensors. This allows for early diagnosis and intervention, particularly in situations requiring urgent intervention or continuous monitoring of chronic diseases. While this system is a fundamental element of a data-driven healthcare infrastructure, it also presents potential problems with energy harvesting, priority management, and interference management. Efficient system operation is only possible with routing definitions that take these issues into account. Therefore, this study proposes a Mixed-Integer Linear Programming (MILP)-based model that aims to optimize scheduling and channel allocation for multi-sensor WBAN environments. The developed model considers dimensions such as energy availability, data priority level, and communication channel capacity, and provides a framework for a smarter, data-driven, priority-aware healthcare system.