This study introduces a Model-Based Deep Reinforcement Learning approach to enhance the effectiveness and transparency of mechanical ventilation treatment in the critical care setting of Intensive Care Units (ICUs). Distinct from conventional model-free methods, our approach benefits from the model-based algorithms’ capability to learn and interrogate dynamics models, enabling better generalization through synthetic data generation and a deeper understanding of the system dynamics. Coupled with Explainable AI (XAI) techniques, we focus on uncovering the underlying mechanisms of patient-ventilator interactions as learned by the AI. Our findings show a significant improvement in treatment efficacy, measured by Fitted Q Evaluation (FQE) metrics, achieved without the need for auxiliary rewards. This advancement not only highlights the potential of model-based reinforcement learning in healthcare but also emphasizes the importance of transparent AI design in healthcare applications.