The reliability of transmission lines under wind loads largely depends on adequate electrical clearances around jumpers. Although the automatic extraction of jumper geometry from light detection and ranging (LiDAR) point clouds and the quantitative assessment of clearance risks under wind deflection are important, a complete, reproducible computational workflow is still lacking. In this paper, we present a procedure for estimating jumper conductor length from airborne LiDAR data and evaluating clearance risks under wind deflection. First, multiscale feature descriptors are used to classify the point cloud around each tower and separate jumper points from tower lattice members and ground returns. Then, the 3D trajectory of the jumper conductor is reconstructed using the exact catenary solution, and the conductor length is obtained directly from its closed-form expression, avoiding the inherent inconsistency in the arc length parameterization of the original parabolic approximation under constant tension. Finally, the reconstructed geometry is used as input to a structural mechanics model. Numerical simulation determines the lateral displacement of the jumper assembly under a specified wind pressure distribution and yields a dimensionless safety margin index, defined as the ratio of the remaining clearance to the required clearance. To address incomplete point clouds caused by occlusion and uneven scanning density, we use iteratively reweighted least squares to stabilize the fitting process. The method is validated on three transmission lines covering different tower types, jumper layouts, and terrains. On the independent validation set, the precision, recall, and F1 score are 0.971, 0.946, and 0.958, respectively, and Cohen’s kappa is 0.955. The mean absolute error and root mean square error of length extraction are 21.4 and 28.7 mm, respectively, and the median relative error is 0.42%. Propagating the uncertainty in the safety margin yields a clearance violation probability, replacing the original deterministic classification into three levels with a probabilistic classification. For a span with a safety margin point estimate of 1.059, the clearance violation probability still reaches 0.320, demonstrating the risk of judging compliance solely from a point estimate. These results provide a quantitative basis for maintenance teams to prioritize jumper maintenance and plan targeted measures to mitigate wind deflection.