Background: Canola (Brassica napus L., abbreviated as B. napus in the following text) is positioned as an economically significant oilseed species in the Canadian agriculture industry. Plant breeders are continuously committing to improving seeds’ protein quality through various breeding programs to increase the economic value of B. napus by capitalizing on the meal co-product of the oil crushing industry for broader utilization. To assess multiple quality traits of a vast number of seed samples within a limited time frame is still a real challenge in the practical B. napus breeding program. Hence, near-infrared reflectance spectroscopy (NIRS) has been proposed to replace traditional wet chemistry analysis methods for predicting seed quality traits in B. napus breeding programs accurately and rapidly. Aim: The current study focused on developing and validating NIRS calibration models for predicting protein and amino acid contents in B. napus seeds and meals; investigating the effects of different spectrometers and sample status on NIRS calibration predictive performance; exploring the applications of the NIRS calibration model on B. napus seeds in practical breeding studies, including discovering the correlation of essential quality traits, as well as investigating the effects of genotype and environment on seed quality. Methods: In total, 480 B. napus seed samples were selected from the 2015 and 2020 cropping year populations; among those, 420 samples were randomly picked and assigned for constructing calibration models, and the rest were used for the external validation study. A partial least square regression technique was performed using the Unscrambler X10.3 software for NIRS model calibration and verification on meal and whole seed basis, with the spectra data obtained from two spectrometers (PerkinElmer DA7250 and PerkinElmer FT9700) and reference crude protein and amino acid contents values determined through traditional wet chemistry methods. Predicted results were used for correlation analysis and variance analysis with SAS 9.4. Results: The calibration models of crude protein and most amino acids except for Tryptophan, Histidine, and Sulphur amino acids showed an acceptable performance with high coefficients of determinations and a low standard error of calibration. The NIR models for Tryptophan, Histidine, and Sulphur amino acids were less accurate, and thus require future research. Besides, PerkinElmer DA 7250 was found to have a similar predictive performance as the PerkinElmer FT 9700, with no significant differences. Specific sample treatments including grinding and Soxhlet defatting led to more uniform sample morphology, which improved the NIRS model forecasting ability on a meal basis. The use of the NIRS modeling within a B. napus breeding study led to a relatively high correlation between essential amino acid and fatty acid profiles through the prediction with the NIRS calibration model. Additionally, different levels of influences of genotype and location and their interactions on B. napus seed quality were discovered. Conclusion: This study illustrated that NIR spectroscopy could be routinely used for measuring the crude protein content of B. napus with great accuracy; the study also indicated that NIRS could rapidly predict amino acid contents of B. napus with acceptable precision on either a meal or seed basis. There is the potential to integrate NIRS technology to feed research and industry level for further B. napus meal capitalization. Significant potential exists to integrate the NIRS technology into established breeding programs, further improving B. napus quality.