A Blockchain and AI Integrated Framework for Dynamic Land Registration and Smart Urban Zoning

Land registration and urban zoning are very important jobs for the government, but in many places, they still have problems with old processes, corruption, and a lack of openness. Urbanization and population growth are putting more and more pressure on systems that need to be smarter and more flexible to keep up with the changes on the ground. This study introduces an innovative, cohesive framework that integrates Blockchain technology with artificial intelligence (AI) to transform the recording of land ownership and the formulation of zoning decisions. The system uses blockchain’s built-in immutability and decentralization to protect land records and zoning permissions, ensuring that everything is clear and lowering the risk of tampering or unauthorized changes. AI models are also used to analyze geospatial data, population growth, and patterns in urban infrastructure to suggest changes to zoning in real time. Smart contracts then carry out these suggestions, letting land reclassification happen automatically and without human bias or delay. The suggested framework was tested on a mix of real and synthetic urban datasets. Results show that the new system works much better than the old ones: processing time is cut by 34%, zoning accuracy is up by 26%, and no tampering was found in simulated blockchain environments. Citizen trust, measured through simulated feedback loops, also increased substantially because of the addition of explainable AI interfaces. This research connects predictive intelligence with secure governance, setting the stage for urban land management systems that are ready for the future, inclusive of all citizens, and resistant to corruption.

@article{R2026,
  title     = {A Blockchain and AI Integrated Framework for Dynamic Land Registration and Smart Urban Zoning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {7},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Shushma R and S R Nagaraja},
  doi       = {10.14569/IJACSA.2026.0170744},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170744}
}