Artificial Super Intelligence (ASI) systems need scalable and efficient decision-making models for effective functioning in complex and dynamic environments. However, existing reinforcement learning approaches have problems such as high computational complexities, low convergence rates, and poor coordination among multiple agents. Thus, this paper proposes a novel Hierarchical Multi-Agent Reinforcement Learning framework coupled with hierarchical multi-agent system architecture. The proposed framework utilizes knowledge about the environment to minimize the search space and stability in learning. Additionally, the hierarchical architecture improves coordination among agents at global, intermediate, and local levels. The system is tested using simulation techniques under artificial environment conditions. The proposed method is implemented using PYTHON software. The experimental results show that the proposed framework improves scalability by 65% to 97%, convergence rate by 30% to 98%, and computational efficiency by 60% to 92%. The experimental results validate that the proposed framework improves scalability and decision-making performance in intelligent systems. This paper emphasizes the benefits of using physics-informed learning coupled with hierarchical multi-agent reinforcement learning for future ASI applications.