Abstract: The arrival of independent drones, powered by Artificial Intelligence (AI), has the implicit to revise diligence ranging from husbandry and logistics to disaster operation and environmental monitoring. This exploration explores the integration of AI technologies in independent drones, fastening on their capabilities for independent decision- timber, navigation, and real- time data processing. The design delves into the core AI factors, including computer vision, machine literacy, and underpinning literacy, which enable drones to perform complex tasks without mortal intervention. By exercising deep literacy models like Convolutional Neural Networks (CNNs) for object recognition and underpinning literacy (RL) for path planning, drones can autonomously descry obstacles, optimize flight paths, and acclimatize to dynamic surroundings. likewise, the design investigates the operation of independent drones in real- world scripts similar as perfection husbandry, disaster relief, and structure examination, demonstrating their eventuality to ameliorate functional effectiveness, reduce mortal threat, and give critical real- time data for decision- timber. Despite these advancements, challenges similar as nonsupervisory enterprises, safety issues, and technological limitations still hamper wide relinquishment. This exploration aims to punctuate both the implicit and the hurdles in the deployment of AI- driven independent drones, furnishing a comprehensive understanding of how AI can enhance their capabilities and shape the future of drone- grounded operations.

Keywords: Autonomous Drones, Unmanned Aerial Vehicles (UAVs), Drone Navigation Algorithms, Artificial Intelligence in Drones

Downloads: | DOI: 10.17148/ IARJSET.2025.121030