morphological and pomological traits. Characterizing this diversity is essential for improving breeding strategies, particularly to enhance fruit quality, yield, and consumer acceptance. This study used statistical tools, including correlation matrix analysis (CMA), principal component analysis (PCA), multiple regression analysis (MRA), and heat map analysis (HMA) to explore trait clustering and genotype differentiation. PCA revealed that the first three components accounted for 31.54% of the total variation. PC1 (11.66%) was driven by traits such as fruit length (0.94), fruit diameter (0.93), fruit weight (0.93), and fruit peel weight (0.93). Heat map analysis grouped variables into four clusters, with traits like total soluble solids (7–22%, mean: 14.72% ± 2.99) and 100-aril fresh weight (19.34–49.46 g, mean: 32.51 g ± 7.93) being critical for marketability. Genotypes were divided into four groups, with subgroup D2 comprising ‘Karimabad-6’, ‘Karimabad-5’, ‘Karimabad-3’, ‘Karimabad-2’, ‘Karimabad-4’, ‘Sangan-2’, ‘Padik-4’, ‘Karimabad-8’, ‘Karimabad-7’, ‘Sangan-1’, ‘Padik-11’, ‘Padik-3’, ‘Padik-2’, ‘Sangan-4’, ‘Sangan-3’, ‘Padik-12’, ‘Karimabad-1’, ‘Sangan-9’, ‘Sangan-8’, ‘Padik-1’, and ‘Daman-9’. MRA identified significant correlations for key traits: fruit weight showed a positive correlation with fruit diameter (β = 0.66, p < 0.00) and fruit length (β = 0.32, p < 0.01), while fruit peel weight showed positive correlations with fruit weight (β = 0.92, p < 0.00) and fruit peel thickness (β = 0.12, p < 0.00). Total soluble solids showed a positive correlation with 100-aril fresh weight (β = 0.80, p < 0.00). These correlations, which were determined to be statistically significant by MRA, are supported by CMA. In the PCA biplot analysis, ‘Daman-2’, ‘Daman-5’, ‘Daman-7’, ‘Karimabad-4’, ‘Padik-9’, ‘Sangan-2’, and ‘Sangan-4’ genotypes were identified as outliers with extreme combinations of fruit traits, falling outside the 95% confidence ellipse, suggesting their potential for breeding programs targeting unique features. This study emphasizes the importance of fruit-related traits, including size, weight, and soluble solids, in genotype differentiation and marketability. Heat map and PCA analyses provided a comprehensive framework for clustering variables and genotypes, identifying actionable targets for breeding. Notably, ‘Sangan-1’, ‘Sangan-2’, ‘Karimabad-1’, ‘Karimabad-2’, ‘Karimabad-3’, and ‘Karimabad-4’ were identified as very soft-seeded genotypes, highlighting their potential for consumer-preferred traits. These findings contribute to improving pomegranate breeding strategies by integrating genetic diversity with agronomic and market demands.
