Caregiving exhaustion is a significant issue among informal caregivers. This exhaustion, driven by the cumulative demands of caregiving, often escalates into burnout, negatively affecting both the caregiver’s well-being and the quality of care provided. The objective of this paper is to propose a model-based reasoning analysis for intelligent caregiver support. The model focuses on understanding and managing caregiving exhaustion through computational dynamics and tailored support mechanisms. To achieve this, the paper employs several threshold parameters to control the action-selection mechanisms, activating specific support actions based on the caregiver’s current state and needs. The effectiveness of this approach is demonstrated through simulations based on three different caregiving scenarios, illustrating how the model can adapt to various challenges and provide targeted support. Finally, the paper verifies the dynamic properties of the proposed model, ensuring its reliability and robustness in real-world applications. This model-based approach offers a promising solution for enhancing the support provided to informal caregivers, ultimately reducing exhaustion and improving the overall caregiving experience.