The performance evaluation of cell towers often requires a manual driving process, which increases the cost of maintenance. This work proposes a UAV-assisted trajectory planning approach to cover cell tower regions for performance measurements. The proposed approach reduces path costs by avoiding repeated visits to correlated areas. In this approach, the data points are divided into cluster regions based on spatial geographical relationships and further re-clustered based on performance indicator values. A correlation matrix is computed for each cluster to identify active and passive regions. Each active point in a region is associated with its corresponding correlated passive points. These active points are used as waypoints for UAV path coverage and data collection. The data from correlated regions are predicted using data collected from active regions. The experimental results demonstrate that this approach reduces the overall path length cost by almost 60%. It is suitable for real-life implementation and adaptable for practical use.