The increasing integration of machine learning into sustainable computing offers new opportunities to evaluate and optimize agricultural practices. Among these, Unmanned Aerial Vehicles (UAVs) have emerged as promising platforms for pesticide application, particularly in low- and ultra-low-volume spraying regimes that can reduce resource use compared to conventional methods. However, systematic benchmarking of UAV spraying against traditional spraying approaches remains limited, especially when considering both environmental and computational dimensions. This study presents a computational intelligence framework that combines life cycle assessment (LCA) with machine learning models to analyze UAV and conventional spraying in mustard production systems. The framework integrates environmental footprints with predictive modeling and statistical validation to capture multiple sustainability indicators, including energy use, water scarcity, and emissions. Using Random Forest and Gradient Boosting regressors, the framework demonstrates how machine learning can reliably approximate life cycle outcomes, while statistical testing confirms the robustness and significance of UAV advantages across locations. The results highlight the potential of machine learning–driven sustainability analysis not only to quantify resource efficiencies but also to support intelligent decision-making in precision agriculture. By situating agricultural spraying within the broader context of computational sustainability, this work contributes a reproducible approach that aligns with energy-aware machine learning and sustainable computing paradigms.