Lentil (Lens culinaris Medik.) is an important legume crop and is considered as a plant-based protein food to fight protein malnutrition and bring health benefits. The current study focused on the development and evaluation of near-infrared reflectance spectroscopy (NIRS) models to predict the protein and amino acid contents in lentils by two NIRS spectrometers: PerkinElmer DA7250 and FT 9700. In total, 361 lentil samples grown in Saskatchewan, Canada, were selected as a calibration set. NIRS calibration models developed by partial least squares (PLS) equation had a satisfactory performance for measuring protein and most amino acids (except for histidine, tyrosine, methionine and cysteine) in lentils with R2C ＞0.65. The sample status, type of spectrometer, and amino acid/protein correlation could influence the NIRS models’ predictive abilities. NIRS models from DA 7250 achieved similar accuracy for the determination of crude protein and amino acids in whole and ground lentils. In the current study, the predictive ability of DA 7250 models and FT 9700 models was not significantly different for all compositions (p<0.05). For amino acids highly correlated to crude protein, NIRS generally predicted them with a higher accuracy. The protein and 18 amino acid contents of 1290 whole lentil samples predicted from DA 7250 models on a dry basis were used as the phenotypic data in the later genome-wide association study (GWAS). GWAS was conducted using phenotypic data from four environments in Saskatchewan, Canada and 266,164 single nucleotide polymorphism (SNP) markers for 324 lentil accessions to identify significantly associated markers. A total of 85 SNP markers were identified to have significant associations with protein and/or 18 amino acids. Only one identical SNP marker (SLCU.2RBY.CHR7_524204079) significantly associated with Val was identified in two environments, and other SNPs were identified only in one environment. These identified SNPs could be studied further to find potential genomic regions or candidate genes. In summary, NIRS could be regarded as a highly promising method for rapid prediction of lentil seed protein and most amino acid contents, and GWAS had a great potential to dissect the genetic basis of these traits in cultivated lentils. Both NIRS technology and GWAS can facilitate breeding programs to enhance lentil seed protein content and quality.