<p>Cold chain logistics (CCL) plays a critical role in ensuring the freshness and integrity of temperature-sensitive goods, such as drugs and food, during transportation. A major challenge in this domain lies in dynamically optimizing delivery routes while ensuring product integrity amid time-varying external and internal temperature conditions. Traditional optimization techniques often fail to provide robust solutions under such complex and real-time constraints. Thus, an intelligent and adaptive routing framework for CCL is proposed by employing Quantum-Aware Particle Swarm Optimization (QAPSO), which enhances conventional particle swarm optimization (PSO) by incorporating quantum computing-inspired mechanisms to expand convergence and solution diversity. The proposed QAPSO-based path planning model formulates CCL as a multi-objective optimization problem that minimizes total transportation time, temperature deviation risk, and energy consumption. Comparative experiments were conducted using benchmark networks and were validated with simulated perishable cargo temperature dynamics. The model leverages Python tools for efficient real-time routing and temperature-aware logistics optimization. The QAPSO algorithm demonstrates superior performance over existing methods in terms of Cost (¥), Distance (km), Running Time (s), and congestion index. Thus, the proposed optimization method provides a powerful and adaptive approach for solving complex cold chain path planning problems under time-varying temperature constraints. Its ability to balance exploration and exploitation leads to more resilient logistics routing, ensuring product safety and cost efficiency.</p>