Rice yield is a complex quantitative trait influenced by multiple interacting components. According to variance analysis, all traits had highly significant differences, indicating enough variability for selection. The largest mean squares were seen in grain yield (F = 245.50***), days to 50% flowering (F = 66.56***), and plant height (F = 37.47***), indicating their significant contribution to overall variation. Grain yield (h2 = 99.60%, GAM = 28.13%) and filled grains per panicle (h2 = 96.40%, GAM = 28.21%) were both strongly controlled by additive control, indicating high heritability across traits and a high potential for direct selection. The influence of non-additive gene action was evident in the low GAM but high panicle length and tiller number heritability. Days until 50% flowering and panicle length were found to be significant positive direct contributors to yield by correlation and path analyses. At the same time, test weight and filled grain number primarily acted indirectly. The tillering ability, filled grain number, flowering duration, and panicle architecture were the main contributors to 61.51% of the total variation in the first three PCs captured by principal component analysis. Three groups of genotypes were created using hierarchical clustering, and each group had its own distinct yield strategy: Cluster I prioritized heavier grains and tillering, Cluster II had longer panicles and higher test weight, and Cluster III had panicle density and grain number. Several high-yielding outliers were found to be promising donor parents (>8.8 t ha⁻¹). This integrated method offers reliable selection indices and useful donor identification by combining genetic parameters, correlation, path, PCA, and clustering. The results directly apply to rice improvement initiatives by ICAR and AICRIP that focus on resilient and high-yielding cultivars for irrigated environments.