As air travel demand increases, passengers often face long wait times, especially during peak periods, leading to frustration and decreased satisfaction. Efficient queue management is crucial, as each passenger must be individually screened at security checkpoints. Optimizing operations can minimize delays and enhance service quality. This study aims to improve queue management, focusing on hand-baggage inspection at King Khalid International Airport’s security checkpoints. A simulation model was initially developed based on data from the airport to manage the hand-baggage inspection queue. The results of this simulation will be used to implement a machine learning model to predict waiting times and allocate passengers to specific queue IDs, optimizing the inspection process and reducing waiting times. The proposed methodology promises to improve service quality, reduce delays, and maintain passenger engagement during the screening process. Expected outcomes include reduced passenger wait times through better resource allocation, improved operational efficiency via optimized real-time data integration, enhanced passenger experience with transparent, real-time queue updates, and the adoption of advanced technologies like IoT, AI, and simulation-based tools at the airport. Additionally, this initiative will position King Khalid International Airport among global best practices. A scalable prototype of the system will also be developed, adaptable to other airports facing similar challenges, ensuring long-term operational benefits and applicability. Implementing this system at King Khalid International Airport will likely boost operational efficiency, improve satisfaction, streamline baggage handling, and serve as a model for future airport optimization efforts.