In the field of Sustainable Agriculture, Agronomy and Food Science, uniform broadcast fertilization leads to severe nitrogen leaching, environmental pollution, and sub-optimal cereal yields. This empirical investigation systematically examines UAV Multispectral Remote Sensing and Machine Learning for Variable Nitrogen Mapping in Wheat Crops through a multi-stage experimental methodology and rigorous quantitative analytical framework. Utilizing unmanned aerial vehicle five-band multispectral imaging, ground-truth leaf chlorophyll sampling, and gradient boosting regression algorithms, data were gathered across multiple operational cycles and validated against established international benchmarks. The statistical and computational results reveal that the XGBoost regression pipeline accurately predicted canopy nitrogen concentrations (R2 = 0.91, RMSE = 0.22 g/100g) two weeks prior to visible symptom onset. Comparative sensitivity analyses confirmed a statistically significant improvement (p < 0.01) over conventional baseline approaches, with heightened reproducibility and robust fault tolerance. These comprehensive findings provide actionable theoretical insights and practical implementation guidelines for precision farming service providers and site-specific fertilizer management systems. Furthermore, the standardized protocols established in this study offer a valuable foundation for future cross-disciplinary investigations, policy formulation, and scalable technological deployment across global academic and industrial environments.