Hybrid cloud environments face persistent challenges in efficient resource allocation due to workload variability, SLA (Service Level Agreement) constraints, and the heterogeneity of infrastructure. This paper introduces a Quantum-Inspired AI (QIAI) model that leverages probabilistic scheduling logic, specifically simulating amplitude-guided exploration and tunneling-based state transitions, to enhance decision-making in complex scheduling scenarios. Implemented using classical hardware, the model is evaluated against traditional heuristics (Round-Robin, Least-Loaded First) and Q-Learning approaches using real-world workload traces from Google, Azure, and Alibaba. Experimental results demonstrate that the proposed model achieves a resource utilization rate exceeding 80% and reduces SLA violations to less than 6%, significantly outperforming the 18.5% violation rate of baseline heuristics. Furthermore, the model exhibits superior efficiency, achieving convergence 35–40% faster than standard reinforcement learning agents. The findings suggest that quantum-inspired models can offer practical and scalable advantages in modern cloud computing systems without the need for actual quantum hardware.