Integrating Sensor Data and GAN-Based Models to Optimize Medical University Distribution: A Data-Driven Approach for Sustainable Regional Growth in Saudi Arabia

Abdullah Addas ^1,2* Muhammad Nasir Khan ^3* Muhammad Tahir ⁴ Fawad Naseer ⁵ Yonis Gulzar ⁶ Choo Wou Onn ⁷

• ¹ College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
• ² Faculty of Architecture and Planning, King Abdulaziz University, Jeddah, Makkah, Saudi Arabia
• ³ Government College University, Lahore, Lahore, Pakistan
• ⁴ Sir Syed University of Engineering and Technology, Karachi, Sindh, Pakistan
• ⁵ Beaconhouse International College, Faisalabad, Punjab, Pakistan
• ⁶ Accounting Department, Business School, King Faisal University, Al-Ahsa 31982, Saudi Arabia
• ⁷ INTI International University, Nilai, Negeri Sembilan Darul Khusus, Malaysia

The regional disparity in higher education access can only be met when there are strategies for sustainable development and diversification of the economy, as envisioned in Saudi Vision 2030. Currently, 70% of universities are concentrated in the Central and Eastern regions, leaving the Northern and Southern parts of the country with limited opportunities. The study created a framework with sensors and generative adversarial networks (GANs) that optimize the distribution of medical universities, supporting equity in access to education and balanced regional development. The research applies an artificial intelligence (AI) driven framework that combines sensor data with GAN-based models to perform real-time geographic and demographic data analyses on the placement of higher education institutions throughout Saudi Arabia. This framework analyses multisensory data by analysing strategic university placement impacts on regional economies, social mobility, and the environment. Scenario modelling was used to simulate potential outcomes due to changes in university distribution. The findings indicated that areas with a higher density of universities experience up to 20% more job opportunities and a higher GDP growth of up to 15%. The GAN-based simulations reveal that redistributive educational institutions in under-representative regions could decrease environmental impacts by about 30% and enhance access. More specifically, strategic placement in underserved areas is associated with a reduction of approximately 10% in unemployment. The research accentuates the need to include AI and sensor technology to develop educational infrastructures. The proposed framework can be used for continuous monitoring and dynamic adaptation of university strategies to align them with evolving economic and environmental objectives. The study explains the transformative potential of AI-enabled solutions to further equal access to education for sustainable regional development throughout Saudi Arabia.