Deep Learning (DL) and Machine Learning (ML) are effectively utilized in various complicated challenges in healthcare, industry, and academia. The Internet of Drones (IoD) has lately cropped up due to high adjustability to a broad range of unpredictable circumstances. In addition, Unmanned Aerial Vehicles (UAVs) could be utilized efficiently in a multitude of scenarios, including rescue missions and search, farming, mission-critical services, surveillance systems, and so on, owing to technical and realistic benefits such as low movement, the capacity to lengthen wireless coverage zones, and the ability to attain places unreachable to human beings. In many studies, IoD and UAV are utilized interchangeably. Besides, drones enhance the efficiency aspects of various network topologies, including delay, throughput, interconnectivity, and dependability. Nonetheless, the deployment of drone systems raises various challenges relating to the inherent unpredictability of the wireless medium, the high mobility degrees, and the battery life that could result in rapid topological changes. In this paper, the IoD is originally explained in terms of potential applications and comparative operational scenarios. Then, we classify ML in the IoD-UAV world according to its applications, including resource management, surveillance and monitoring, object detection, power control, energy management, mobility management, and security management. This research aims to supply the readers with a better understanding of (1) the fundamentals of IoD/UAV, (2) the most recent developments and breakthroughs in this field, (3) the benefits and drawbacks of existing methods, and (4) areas that need further investigation and consideration. The results suggest that the Convolutional Neural Networks (CNN) method is the most often employed ML method in publications. According to research, most papers are on resource and mobility management. Most articles have focused on enhancing only one parameter, with the accuracy parameter receiving the most attention. Also, Python is the most commonly used language in papers, accounting for 90% of the time. Also, in 2021, it has the most papers published.

深度学习 (DL)和机器学习 (ML)被有效地用于应对医疗保健、工业和学术界的各种复杂挑战。由于对广泛的不可预测情况的高度可调整性，最近出现了无人机互联网(IoD) 。此外，无人驾驶飞行器 (UAV)可以在多种场景中得到有效利用，包括救援任务和搜索、农业、关键任务服务、监视系统等，这归功于低移动性、延长无线覆盖区域的能力等技术和现实优势，以及到达人类无法到达的地方的能力。在许多研究中，IoD 和 UAV 可以互换使用。此外，无人机提高了各种网络拓扑的效率，包括延迟、吞吐量、互连性和可靠性。尽管如此，无人机系统的部署提出了各种挑战，这些挑战与无线介质固有的不可预测性、高机动性以及可能导致拓扑快速变化的电池寿命有关。在本文中，IoD 最初是根据潜在应用和比较操作场景来解释的。然后，我们根据其应用对 IoD-UAV 世界中的 ML 进行分类，包括资源管理、监视和监控、目标检测、电源控制、能源管理、移动性管理和安全管理。本研究旨在让读者更好地了解 (1) IoD/UAV 的基础知识，(2) 该领域的最新发展和突破，(3) 现有方法的优缺点，以及 (4)需要进一步调查和考虑的领域。结果表明 电源控制、能源管理、移动性管理和安全管理。本研究旨在让读者更好地了解 (1) IoD/UAV 的基础知识，(2) 该领域的最新发展和突破，(3) 现有方法的优缺点，以及 (4)需要进一步调查和考虑的领域。结果表明 电源控制、能源管理、移动性管理和安全管理。本研究旨在让读者更好地了解 (1) IoD/UAV 的基础知识，(2) 该领域的最新发展和突破，(3) 现有方法的优缺点，以及 (4)需要进一步调查和考虑的领域。结果表明Convolutional Neural Networks (CNN) method is the most often employed ML method in publications. According to research, most papers are on resource and mobility management. Most articles have focused on enhancing only one parameter, with the accuracy parameter receiving the most attention. Also, Python is the most commonly used language in papers, accounting for 90% of the time. Also, in 2021, it has the most papers published.