Background: Psychosis is a severe mental health condition requiring continuous monitoring and timely interventions. Traditional approaches often fail to adapt to real-time fluctuations in a patient’s mental state, limiting their effectiveness. This study introduces a Partially Observable Stochastic Game (POSG) framework to model interactions among key stakeholders, including patients, caregivers, healthcare professionals, and external observers. By incorporating multi-agent decision-making and belief-driven strategies, the model aims to improve psychosis prevention and patient stability. Methods: The proposed POSG model represents the patient’s mental state as healthy, at risk, or psychotic. Stakeholders select actions such as therapy, medication, or passive monitoring based on noisy observations. Bayesian inference updates belief states, ensuring informed decision-making. A reward system quantifies intervention effectiveness, while a Nash equilibrium ensures optimal strategic interactions. Reinforcement learning (RL) refines policies over time, enabling adaptive decision-making. The model integrates dynamic feedback loops, allowing stakeholders to adjust strategies based on observed patient responses, fostering personalized and effective interventions. Results: Simulation results indicate that multi-agent interactions and belief-based strategies enhance patient stability. By dynamically refining strategies, stakeholders reduce unnecessary interventions while improving recovery outcomes. The model demonstrates that coordinated decision-making among multiple stakeholders leads to more stable and predictable intervention success. Conclusions: The POSG framework offers a structured and adaptive approach to psychosis prevention, optimizing intervention effectiveness through multi-agent coordination. The results suggest that AI-driven mental health monitoring can pave the way for more personalized and proactive interventions, reducing the burden of psychosis on individuals and healthcare systems. This study highlights the potential of game-theoretic models in mental health applications, contributing to the development of data-driven intervention techniques.