Highway monitoring is crucial to ensuring the safety, efficiency, and maintenance of road networks. This process allows for the identification of dangerous traffic, rapid detection of accidents, management of vehicle flow, and route optimization, among other benefits. For public agencies, monitoring facilitates the collection of traffic data, which is essential for complying with laws and preventing incidents. However, the large volume and speed of data can make processing difficult, impacting decision-making. In this context, parallel processing stands out as an effective solution, especially in image analysis, through the division of subtasks and the use of GPU. Using virtual machines with different memory and processing resources, the delays in image processing were analyzed, using queueing theory to propose machine configurations that allow processing close to real time to minimize the delays generated during the processing of these images. The analysis showed that the use of GPU significantly reduces processing time; however, the use of machines with CPU obtained satisfactory performance from the orchestration of different machines in parallel. It is concluded that parallel processing, combined with queueing theory, optimizes highway monitoring, balancing performance and resource costs.