This paper presents the Clustered Temporal Path Planning (CTPP) algorithm, designed to optimize drone swarm operations in dynamically updated digital twin environments. CTPP integrates the DB-SCAN clustering technique with trajectory planning, leveraging time data to identify and cluster regions likely to become outdated. The algorithm calculates the necessary waypoints within each cluster and uses a Traveling Salesman Problem (TSP) solver to determine the optimal path, guiding drones efficiently while minimizing unnecessary travel. This approach enhances system efficiency and reduces 3D update latency. We evaluated CTPP through three experimental sets, varying area sizes and drone counts, including scenarios with fixed drone numbers and increasing area sizes to test scalability. The results show that CTPP significantly outperforms existing algorithms, achieving improvements of 66.47% and 18.41%, respectively. This leads to a substantial improvement in reducing the 3D update latency of digital twin environments.