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The world is on the brink of an era of Unmanned Aerial Vehicles (UAVs), widely known to public as drones, where we will get to experience multiple UAVs flying in the national airspace carrying out diverse tasks such as monitoring, surveillance, product deliveries, law enforcement, fertilizing crop fields, aerial photography, and transport. In such scenarios, where multiple UAVs are flying in a smaller airspace, there is a possibility of collisions, path overlaps, mix-ups, and uncertainties as far as their flying routes are concerned. These flying routes could be inside constructed air corridors where the UAVs would be allotted to fly, similar to the air corridors of commercial aircraft. There is a growing need to identify and construct these air corridors for UAVs to fly in their respective corridors to avoid such mishaps as is what is done with commercial airplanes. The airplanes fly in their designated air corridors from one location to another without any uncertainty. It would be really useful to devise and design such a system for multiple UAVs as well, that would be able to construct air corridors for them to fly through. This served as the primary motivation behind proposing a novel approach to estimate and visualize air corridors for autonomous multi-UAV flights in an airspace. In addition to it, we studied various popular uncertainty visualization techniques and came up with a cutting-edge way to incorporate uncertainty into the visualization of the air corridors. Furthermore, we provide a standalone web application with a user-friendly graphical user interface (GUI) developed using HTML5, CSS3, JavaScript and an open-source JavaScript library for visualizing world-class 3-D maps called CesiumJS. Subsequently, we present the estimation and visualization results and discuss possible application areas where the proposed technique could be put to use. Finally, we discuss the summarized research findings and future research directions.