### Organization  
The rest of the article is arranged as follows. Section 2 presents the details of the existing schemes to tackle the COVID-19 pandemic. Section 3 describes the UAV functionality to address the threefold functionalities for the fight against COVID-19 and future pandemics. Section 4 describes the proposed scheme that deploys the required functionality through UAV-based ecosystems. Section 5 discusses the task offloading scheme for UAVs, where data-intrinsic tasks are deputed to edge nodes, which learn through the federated learning process. Section 6 presents the detection of persons for thermal monitoring and surveillance in moving FoRs. Section 7 highlights the open issues and research challenges in the integration of UAVs and 6G through the proposed scheme. Section 8 validates the efficiency of the proposed scheme through performance simulations, and finally, Section 9 concludes the article. [PERSON] et al. [31] designed a real-time patent for an IoT-envisioned trash system, where trash collectors are equipped with pressure sensors, which measure the pressure on a trash collection system. The system communicates with UAVs, which monitor the garbage locations and transmit them through local transceivers. This type of real setup is highly essential as uncollected waste (dry and wet waste) can lead to contamination of water and land sources and becomes a breeding ground for lung and respiratory disorders. Thus, COVID-19 rates might shoot up, and the designed patent would indirectly benefit the implementation of 3 FFs and avoid the unprecedented rise of COVID-19 positive cases, which would bottleneck the resource requirements to implement 3 FFs. [PERSON] et al. [22] present a crowdsensing scheme for densely populated regions, where the monitoring is supported through UAVs, and a water cycle algorithm (WCA) is exploited, which sends a short message service (SMS) to administrator nodes informing them of congestion. To strengthen the decision-making structure, the ResNet 50 model is used, which suggests alternate routes to the user in dense and blocked roads. A deep transfer learning (DTL) mechanism detects crowds and builds appropriate actions required to disperse the crowd. Experimental analysis shows an improvement in average accuracy, which is \(\approx\)96.55%, in challenging UAV flying scenarios. [PERSON] et al. [23] presented an applicative study on the 6G use cases to solve the diverse problem in different domains. Device-to-device communication with 6G and multi-access edge computing are presented to allow digital transformation. The analysis is carried out at the United Nations institute of advanced research training on 6G integration with future networks. In UAV vaccine delivery, [PERSON] et al. [19] presented a scheme, named _SanjeeVni_, that handles massive vaccine user registration at model centers via the Solana blockchain. As Solana has a high transaction rate of 65,000 transactions in 1 second (s), the addressing scheme is quite resilient and scalable. This assures that illegal vaccine hoarding is eliminated. A consortium setup is preferred, where vaccine details are shared with users and nodal centers. For latency in vaccine distribution, the scheme is presented against the backdrop of 6G communication that offers intelligent AI edge offloading, UAV movement, and ground station control. In parametric analysis, vaccination cost, 6G spectral efficiency, and energy consumption in traffic offloading are discussed, where an improvement of 86% is obtained in latency, as well as a 76.25% reduction in UAV energy consumption. Authors in [24] proposed a social distancing UAV scheme for COVID-19 over a pedestrian network where the foot movements and head positions are classified in real-time over the Peleenet backbone. On the Merge-head dataset, an average precision of 92.22% is obtained at a 76 frames per second capture. A You Look Only Once version 3 (YOLOv3) model with a single-shot detector (SSD) is utilized.