The dynamic growth of cities and rising mobility demand create major challenges in road traffic management, including congestion, accidents, and environmental impacts. Intelligent Transport Systems (ITS) support monitoring, analysis, and optimization of traffic, but weather conditions remain a significant factor influencing vehicle speed, density, and flow. This study analyzes the impact of weather on traffic dynamics in Prague, focusing on changes in intensity, average speed, and density. Data from traffic detectors on a key road section, collected between October 3–10, 2022, enabled comparisons across different times of day and week. QVK (speed, density, volume) modeling was applied to identify traffic patterns and their correlation with weather. The results show that rainfall and reduced visibility significantly decrease average vehicle speed and increase congestion. Clear differences between weekdays and weekends were also observed: weekdays show higher intensity during rush hours, while weekends have generally lower volumes. The findings provide practical insights for transport planners and decision-makers. They highlight the importance of integrating weather factors into traffic management strategies and ITS design. Moreover, the study emphasizes the value of long-term data analysis and predictive technologies, such as artificial intelligence and machine learning, to enhance urban transport efficiency and safety.