Stroke recovery increasingly demands a reconceptualization of the brain as a dynamic network system. This opinion paper advocates for a network-oriented neurorehabilitation paradigm, grounded in the understanding that functional recovery relies on the reorganization of distributed, interconnected neural systems across the central nervous system. While traditional rehabilitation approaches often target isolated impairments, a broader challenge is in the insufficient specification and documentation of therapeutic processes and their underlying mechanisms, which are often incompletely described and difficult to systematically replicate or compare across studies. We argue that effective therapy must integrate cognitive, motor, sensory, as well as perceptual, emotional, and motivational processes through strategically designed, goal-directed exercises that activate residual plastic hubs and promote adaptive connectivity. From this perspective, rehabilitation becomes a process of guided network modulation, in which task demands are dynamically adjusted to the patient's evolving capacities in order to optimize engagement and learning. Importantly, this approach responds to the need for clearer identification of treatment components, mechanisms of action, and active ingredients, enabling a more transparent and theoretically grounded description of therapeutic interventions. To illustrate these principles, we highlight key features of Cognitive Multisensory Rehabilitation (CMR) and its subsequent development into the Comparison of Actions (CTA) approach, which emphasizes action simulation and comparison within a network-oriented framework. Building on this rationale, we present a Model Structure for Network-Based Therapeutic Exercises, consisting of a five-phase model derived from a network-oriented interpretation of the CMR/CTA framework. This model provides an operational structure for translating theoretical principles into clinical practice, while making the underlying cognitive and sensorimotor processes of therapeutic action explicitly identifiable and parametrically modulable, thereby supporting greater clarity, reproducibility, and consistency in therapeutic design and implementation. In addition, we discuss how emerging technologies, such as robotics and virtual reality, may further support network engagement by enabling the controlled delivery, monitoring, and adaptation of these processes within structured therapeutic frameworks. By aligning therapeutic planning with network neuroscience, this model offers a structured yet flexible approach to personalized rehabilitation. It allows therapeutic tasks to be systematically adapted across distinct phases based on the patient's neuropsychological profile, sensorimotor status, and functional goals, while making treatment components and mechanisms more explicit and clinically interpretable. In doing so, it supports a more individualized, functionally relevant, and theoretically grounded approach to post-stroke neurorehabilitation.