This study aimed to demonstrate the efficacy of drone-assisted crop monitoring in precision agriculture by evaluating the relationships between the NDVI, leaf area index (LAI), and leaf nitrogen content (LNC) in three wheat varieties (DBW-187, HD-3086, PBW-826) under eight nitrogen treatments (N0–N210). The NDVI was derived from drone-based multispectral imagery at the flowering (90 DAS) and grain-filling (108 DAS) stages. Strong correlations were observed between the NDVI, LAI, and LNC, with the R² values improving from 0.78–0.86 at flowering to 0.88–0.90 at grain filling. These findings highlight the potential of drone-derived indices for efficient crop monitoring, resource use optimization, and yield prediction in precision agriculture.