The present investigation was conducted during the wet season of 2024 at Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh, to assess principal component analysis (PCA) and genetic diversity among fourteen nutrient-rich rice genotypes evaluated in a randomized block design. PCA revealed that the first four principal components had eigenvalues greater than one and together accounted for 71.81% of the total variation. PC1 (24.95% variation) was primarily influenced by days to flowering (DTF), head rice recovery (HRR), grain yield (GY), plant height (PH), and amylose content (AMY), while PC2 (20.80%) was associated with panicle length (PL) and γ-aminobutyric acid (GABA). PC4 contributed 11.20% of the variability, with major loadings from number of tillers (NT), panicle per square meter (PSQM), and zinc content (Zn). Cluster analysis grouped the genotypes into three clusters: Cluster I and Cluster II contained six genotypes each, and Cluster III comprised two genotypes. Cluster I genotypes exhibited higher mean values for DTF, HRR, PH, NT, PSQM, and AMY, primarily representing yield-attributing traits. In contrast, Cluster III genotypes showed early flowering along with higher mean values for PL, Zn, Grain protein (P), and GABA, representing yield and quality-enhancing traits. Genotypes from these clusters, possessing complementary and desirable attributes, can serve as potential donors in future hybridization programs aimed at developing nutrient-rich, high-yielding rice varieties.