In the dynamic realm of air travel, the efficient functioning of airport checkpoints is paramount to ensure a smooth and expedient passenger experience. This research paper introduces a comprehensive system that utilizes Internet of Things (IoT) technology and machine learning for the analysis of dwell times at various checkpoints within airports. The primary objective is to enhance the utilization of airport touch points, reduce passenger queuing times, and overall improve airport operational efficiency. The project involves the deployment of IoT sensors at key touch points, encompassing check-in counters, entry gates, Security and Health Assessment (SHA) checkpoints, immigration and customs, and boarding gates. The IoT sensors collect real-time dwell time data from passengers, considering their arrival and departure times. The collected data is then processed using machine learning algorithms, especially linear regression, SVM, KNN, random forest, foretelling dwell times in light of temporal parameters. The research paper discusses the step-by-step implementation process, from project planning and technology stack selection to pilot deployment and ongoing maintenance. Results from the pilot deployment demonstrate the system’s effectiveness in reducing dwell times and streamlining checkpoint operations. The paper concludes by highlighting the significance of this approach in improving passenger experience and the adaptability of the system to diverse airport environments, providing a foundation for future research and advancements in airport operational enhancing.