<p>Electrical power systems are susceptible to several damaging effects, potentially leading to faults reaching safety limits and posing critical operational risks. Traditionally, manual inspection has been employed to detect such faults; however, this method is inefficient—being both time-consuming and lacking precision. Once a fault is observed, prompt recognition becomes paramount to ensure the safe resumption of system operations. Addressing this issue, Drone-based strategies have proven to be effective in recognizing these irregularities. In particular, intelligent inspection methods have gained much attention in the past few years, evidenced by a remarkable 1000% surge in the adoption of deep learning techniques and a 420% surge in the utilization of drones. In this survey, we explore the main strategies of evolving Drone-based intelligent inspection methods for fault recognition in electrical power systems. The application of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology revealed a total of 36 papers in the literature on the subject. As primary results, a synthetic description of the works was provided, unveiling the most frequently used algorithms, fault types, and sensors, along with their relationships established through a heatmap diagram. The identification of literature gaps and future research directions reveals the path for further exploration, including the need for more robust algorithms to improve fault detection accuracy, techniques to mitigate the impact of blurred images, methods for detecting multiple faults simultaneously, advancements in real-time processing, increased automation for field deployment, and the development of more comprehensive and diverse datasets.</p>