Improving the quality of the appearance of rice is critical to meet market acceptance. Mining putative quality-related genes has been geared towards the development of effective breeding approaches for rice. In the present study, two SL-GWAS (CMLM and MLM) and three ML-GWAS (FASTmrEMMA, mrMLM, and FASTmrMLM) genome-wide association studies were conducted in a subset of 3K-RGP consisting of 198 rice accessions with 553,831 SNP markers. A total of 594 SNP markers were identified using the mixed linear model method for grain quality traits. Additionally, 70 quantitative trait nucleotides (QTNs) detected by the ML-GWAS models were strongly associated with grain aroma (AR), head rice recovery (HRR, %), and percentage of grains with chalkiness (PGC, %). Finally, 39 QTNs were identified using single- and multi-locus GWAS methods. Among the 39 reliable QTNs, 20 novel QTNs were identified for the above-mentioned three quality-related traits. Based on annotation and previous studies, four functional candidate genes ( , , , and ) were found to influence AR, HRR (%), and PGC (%), which could be utilized in rice breeding to improve grain quality traits.