Driven by globalization and information technology, modern logistics industry is becoming a key link connecting developed and developing countries. Modern logistics improves efficiency and optimizes supply chain management through information technology, networking, and intelligent means. The purpose of cigarette logistics is to ensure that cigarette products can be delivered to their destination quickly, safely and accurately. Delivery path optimization is the core of cigarette logistics, which improves service efficiency and customer satisfaction by reducing delivery time. Ant colony algorithm, as a heuristic algorithm, was widely used in fields such as TSP, scheduling and path planning. The application of ant colony algorithm in optimizing the distribution path of cigarette logistics was explored. Firstly, the concepts related to cigarette logistics and distribution path optimization was introduced, and then the theory and implementation process of ant colony algorithm were deeply analyzed. Next, an ant colony algorithm model was constructed to solve the problem of cigarette logistics distribution, and the algorithm performance was evaluated through simulation analysis. Finally, taking DH Tobacco Company in Yunnan Province as a case study, the optimization strategy of its cigarette logistics distribution path was explored. Using Matlab simulation modeling, research has shown that ant colony algorithm can significantly optimize cigarette logistics distribution paths, reduce costs, improve efficiency, and provide technical support for the industry.