Traffic monitoring is an important part of congestion mitigation and traffic management. This study proposed congestion mitigation approach based on the premise that a short interval analysis (one to five minutes) would be sufficient to manage congestion defined by exceeding the road capacity and is characterized by high vehicular density and vehicle speeds that are lower than the desired speeds. A number of interconnected, self-managed drones were utilized to establish a deployable system to perform immediate monitoring and assessment for traffic conditions to infer if congestion is approached. In order to detect vehicles, a machine learning technique of Convolutional Neural Network (CNN) was utilized that used a single neural network to the full image which divided the image into regions and predicted bounding boxes and probabilities for each region. Two neural networks were trained using only the five desired classes to be detected: car, bus, truck, motorbike, and bicycle. Drone cameras were calibrated using real values observed in Birmingham, AL in 2019, compared to their apparent values in images to track any detected vehicles. The features correlated to traffic congestion were reproduced utilizing a traffic simulation model and the proposed methodology was tested by collecting and investigating video images from drones. In addition, detection limitations of drones were tested using multiple videos from Raleigh, NC as well as Georgia DOT to estimate the minimum size of detection.