International Journal of Scientific Research and Engineering Development

Conventional land classification and zoning methodologies are characterized by significant manual effort, prolonged decision cycles, and susceptibility to inconsistency arising from reliance on outdated geographic information systems (GIS). These limitations impede timely and evidence-based urban planning. This paper presents SmartZoneX, an artificial intelligence and machine learning (AI/ML)-based geospatial land classification system designed to automate and accelerate zoning decisions. The system integrates realtime geospatial data streams—including Normalized Difference Vegetation Index (NDVI) values, soil properties, rainfall patterns, elevation data, and infrastructure proximity metrics—sourced from open APIs such as OpenStreetMap, SoilGrids, and NASA SRTM. A feature engineering pipeline transforms these heterogeneous inputs into a structured feature vector, which is subsequently processed by ensemble machine learning models, specifically Random Forest and XGBoost classifiers, to predict land-use categories comprising Residential, Industrial, Farmland, Forest, and Greenfield. The system incorporates a graphical user interface (GUI) developed with the Tkinter framework, enabling interactive parcel selection and real-time visualization of classification outcomes with associated confidence scores. Comprehensive testing comprising unit, integration, system, performance, and usability evaluations demonstrated system stability, with all 20 primary test cases passing. SmartZoneX offers a scalable, reliable, and operationally deployable solution for urban planners, governmental authorities, researchers, and environmental organizations.

Shifa Bilal Tamboli, Simeen Phiroj Mulani, Arman Tajuddin Shiakh,"SmartZoneX: An AI/ML-Based Geospatial Land Classification System for Automated Zoning and Urban Planning" International Journal of Scientific Research and Engineering Development, V9(2): Page(2348-2354) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.