Researchers have explored a hybrid approach that combines biomimetic algorithms inspired by Physarum polycephalum and ant colony optimization (ACO) to address the challenges in UAV logistics. The investigation focuses on optimizing delivery routes, particularly in complex and dynamic environments such as urban settings or during emergency response scenarios. Physarum polycephalum is known for its ability to explore and map out networks efficiently, while ACO specializes in route optimization through pheromone-based reinforcement. By integrating these two models, a more adaptable and scalable solution to UAV route optimization is proposed. This review identifies the hybrid algorithm as the most promising method due to its ability to balance exploratory breadth with rapid convergence to optimal paths. The potential for enhanced adaptability, scalability, and real-time optimization in UAV logistics is discussed, particularly in scenarios like pandemic-related deliveries. Several hypothetical solutions are proposed, with a focus on multi-stage optimization and dynamic obstacle avoidance. The effectiveness of the Physarum-ACO hybrid approach in closing gaps in current UAV logistics is addressed, along with suggestions for further research. These include refining convergence speed, incorporating machine learning for predictive optimization, and conducting real-world tests to validate the algorithm’s performance. The potential for applying this model in other fields, such as smart cities or autonomous vehicle navigation, is also highlighted.