The rapid expansion of power transmission infrastructure necessitates the development of efficient and accurate inspection methods. This paper proposes an autonomous positioning model for Unmanned Aerial Vehicles (UAVs) that can detect power line insulators on transmission lines to address this need. The proposed model leverages machine learning algorithms for autonomous detection of insulators. To determine the optimal stopping point and safety distance between the UAV and the insulator, a mathematical model is presented that utilises the captured images and the machine learning algorithm. A simulation model is utilised to verify the proposed model, ensuring that the UAV moves to the best-predicted position. The machine learning algorithms are utilised to identify and calculate the length of power line insulators. A set of labelled insulator images is trained in the selected machine learning algorithm, enabling it to accurately determine the length of insulators in new images. The mathematical model considers the size of the insulator in the image to calculate the safety distance between the UAV and the power line insulator, while also determining the optimal image shooting coordinate. MATLAB’s Simulink software is utilised to leverage the UAV’s navigation and control systems, enabling it to move to the best position for capturing high-quality photos of the power transmission lines. The model also considers environmental conditions and operational constraints for optimisation. The proposed autonomous positioning model has undergone extensive simulation to demonstrate its effectiveness. Furthermore, the autonomous positioning of the UAV reduces human intervention, minimises inspection time, and increases efficiency and cost-effectiveness.