Precision agriculture has increasingly relied on advanced sensing, machine learning, and cloud-based analytics to improve farm management. The most existing systems remain prediction-centric, offering limited support for structured decision-making, trade-off analysis, and uncertainty management. This paper proposes multi-layered visual analytics–oriented decision intelligence framework that explicitly bridges predictive analytics and actionable agricultural decisions. The framework integrates heterogeneous data ingestion, predictive modeling, multi-objective and risk-aware optimization, interactive visual analytics, and human-in-the-loop feedback within a unified architecture. Unlike conventional decision support systems that treat visualization as an output layer, visual analytics is embedded as a core reasoning component enabling trade-off exploration, uncertainty interpretation, and adaptive decision refinement. The framework is evaluated using real-world agricultural datasets and region-specific case studies involving rice, sugarcane, and cotton cropping systems. Experimental results demonstrate statistically significant improvements in decision quality, including 15–27% yield gains, 18–31% improvements in water-use efficiency, and substantially reduced decision variance under climatic stress compared to prediction-only and rule-based baselines. The findings demonstrate that integrating analytics, optimization, visualization, and human expertise is essential for robust, transparent, and sustainable precision agricultural management.