<p>The rise in complexity of large scale IoT-enabled drone logistics networks would require intelligent computing systems that can autonomously make optimal scheduling and routing decisions. Since drones are highly important in real-time delivery and surveillance services, the planning of routes and flexible scheduling are critical to the improvement of the reliability and scalability of the operations. The current means have shortcomings in terms of lack of flexibility, routing algorithms that are fixed and lack the responsiveness to the dynamic aspects of the environment like weather, air traffic and the varying need to deliver goods. To eliminate these constraints, this paper proposes the Reinforcement Learning-Based Adaptive Route Optimization (RL-ARO) model, which incorporates Deep Reinforcement Learning into the IoT-based contextual information to optimize routes and schedule. The framework allows multi-agent drones to jointly discover the best flight paths because they constantly adapt to the current network conditions, resource limitations, and energy efficiency needs. The suggested RL-ARO algorithm is implemented in a smart healthcare delivery system for the real-time medical supply and vaccine delivery environment with minimal latency and energy usage. The results of the experiment show that the framework can dramatically increase the efficiency of the route, decrease the delivery time by more than 35%, and increase the resilience of the system in large-scale and dynamical domains. The proposed method achieves the average time gap after 150 missions, energy consumption reduction (850 wh), route optimality (&gt; 90), adaptation response time (6&#xa0;s), packet deliver ratio (96–99).</p>