<![CDATA[Agricultural regional planning plays a pivotal role in ensuring food security, environmental sustainability, and balanced regional development in line with the Sustainable Development Goals (SDGs). However, conventional planning approaches often rely on fragmented datasets and static decision-making processes, limiting their ability to respond to dynamic agricultural and environmental conditions. This study proposes a Big Data Analytics-based Precision Agricultural Regional Planning framework that integrates multi-source datasets, including satellite remote sensing, climate records, soil characteristics, topography, land use, socioeconomic indicators, and Internet of Things (IoT) sensor data. The framework employs advanced machine learning algorithms, Geographic Information Systems (GIS), and spatial analytics to identify agricultural land suitability, predict crop productivity, optimize resource allocation, and assess climate-related risks. A multi-criteria decision-making (MCDM) approach is incorporated to prioritize strategic agricultural development zones based on economic, environmental, and social sustainability indicators. The proposed model is validated using regional agricultural data through predictive accuracy metrics, spatial validation, and scenario analysis. The findings demonstrate that integrating Big Data Analytics significantly improves planning precision, enhances land-use efficiency, reduces production risks, and supports evidence-based policymaking. Furthermore, the framework contributes directly to SDG 2 (Zero Hunger) by improving agricultural productivity, SDG 9 (Industry, Innovation and Infrastructure) through digital agriculture technologies, SDG 12 (Responsible Consumption and Production) via optimized resource utilization, SDG 13 (Climate Action) by strengthening climate resilience, and SDG 15 (Life on Land) through sustainable land management. This research provides a scalable and intelligent decision-support framework for policymakers, regional planners, and agricultural stakeholders seeking to implement precision agriculture strategies for sustainable regional development.]]>
