Optimizing drone-based delivery requires assignment models that handle uncertainty in dynamic environments. Traditional algorithms often struggle with multi-criteria variability such as weather, demand shifts, and operational constraints. This paper introduces the Circular Intuitionistic Fuzzy Hungarian Algorithm (CIFHA), extending the classical Hungarian method by integrating Circular Intuitionistic Fuzzy Triples (CIFTs). The proposed framework models cost, priority, and feasibility within a circular intuitionistic fuzzy context, better reflecting nonlinear dependencies in drone logistics. CIFHA is applied to an autonomous drone delivery system, optimizing drone-to-task assignments under uncertainty in flight durations, package urgency, and environmental conditions. By using CIFTs, the method enhances flexibility and robustness against disruptions. Experimental results demonstrate that CIFHA improves assignment efficiency and reliability compared to standard intuitionistic fuzzy models. This methodology offers a powerful tool for drone logistics, smart transport, and automated resource allocation. It sets a foundation for future research on multi-agent coordination under uncertainty, particularly in real-time systems and intelligent logistics.