In the present research, 20 sugar beet genotypes were examined in a randomized block design for sugar content (SC), white sugar content (WSC), potassium amount (K), sodium content (Na), alpha-amino nitrogen (AN), alkalinity coefficient (ALC), extraction coefficient of sugar (ECS), molasses sugar (MS), plant number at harvesting (PNH) and storage root yield (RY) using standard protocols. The genotype × trait (GT) method was utilized to investigate the interaction patterns, revealing that it accounted for 66% of the total observed variability. The biplot is categorized into four sections, and in the section of genotype G3, traits RY, PNH, AN and Na were located, so G3 had the highest magnitudes for these traits while in the section of genotype G19, traits SC, WSC, ECS were located. Genotype G16 was the winner in MS and K, while G9 was the winner in ALC. Thus, G3 following genotypes G4 and G5, was the most favorable regarding root yield while G19 following genotypes G11 and G13, was identified as the best for sugar-related traits. The other biplot demonstrated that SC, WSC, and ECS were positively correlated, while there was a negative correlation between them and K, MS, AN and Na. Also, RY and PNH were positively correlated, while there was a negative correlation between them and ALC. According to ideal genotype properties, G3 followed G4, G1 and G12 were more favorable while according to ideal trait properties, RY and PNH were favorable following AN and Na. This study offers new perspectives on the varying performance of genotypes across attributes and emphasizes the effectiveness of the GT biplot model in discovering superior genotypes. The selected genotypes—G3 and G4 for root yield, and G19 and G11 for sugar-related traits—present viable possibilities for commercial endorsement to farmers. These findings highlight the necessity of incorporating sophisticated multivariate techniques such as the GT biplot into breeding initiatives to improve sugar beet yield and quality.