International Journal of Neutrosophic Science IJNS 2690-6805 2692-6148 10.54216/IJNS https://www.americaspg.com/journals/show/3966 2020 2020 College of Administrative & Financial Sciences, Gulf University, Bahrain Tanvir Tanvir Tashkent State University of Economics, Uzbekistan Tojiyev Rakhmatilla Amity University in Tashkent, Uzbekistan Danish Ather Computer Science Department, Applied College, Imam Abdulrahman Bin Faisal University, Saudi Arabia Rubina Liyakat Khan School of Computer Science and Engineering, Lovely Professional University, Punjab, India Tiyas Sarkar School of Computer Science and Engineering, Lovely Professional University, Punjab, India Manik Rakhra This paper proposes a novel hybrid framework that integrates Neutrosophic Logic with Artificial Intelligence (AI) for robust spatiotemporal modeling of urban land parcel transactions. The approach captures the uncertainty, inconsistency, and incompleteness often found in public land auction data through the application of neutrosophic triplets, defined by degrees of truth, indeterminacy, and falsity. Using longitudinal transaction records from Tashkent, the model transforms raw data into neutrosophic representations and feeds them into a Long Short-Term Memory (LSTM) network for forecasting. The enriched feature space enhances interpretability and prediction accuracy across administrative zones. Experimental evaluations demonstrate the superiority of the proposed Neutrosophic-AI model over conventional methods in terms of forecasting precision and uncertainty handling. This study offers a foundational contribution to neutrosophic-based urban analytics and supports transparent digital governance frameworks. 2026 2026 125 138 10.54216/IJNS.270112 https://www.americaspg.com/articleinfo/21/show/3966