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The effect of traits and the interaction of genotype × environment (GE) is one of the major challenges in detecting traits and genotypes with outstanding performance and stability through various stresses and years. The objective of this study was to identify the genetic influence traits of wheat, and genotypes with outstanding performance and stability under different environmental stress. The trials were carried out in two consecutive seasons with three treatments (optimal irrigation, limited irrigation, and heat stress), totaling six test environments at two different locations. After observing the importance of GE interaction, and the statistical significance for all studied traits, multivariate analysis was applied using stepwise regression (SR) for detecting influenced traits, and AMMI, AMMI’s stability values (ASV), yield stability index (YSI), superiority and GGE biplot methods to identify the genotype’s phenotypic stability. SR analysis showed that nine out of 22 traits have contributed significantly to grain yield (GY), which varied according to the environment. Equations of the models (GY) regression coefficient values reflected the importance seven of them have on a significant positive correlation on GY. The study confirmed the importance of AMMI and GGE biplots in decoding the GEI based on GY data. AMMI1 biplots showed that the three environments E1, E4, and E6 were the stronger interacting environments than E2, E3, and E5, in which the interaction was weak. YSI, superiority analysis, and superiority multi-trait analysis scores were largely compatible. YSI scores described the six genotypes viz, G5 (DHL26), G12 (DHL29), G10 (DHL01), G18 (Sakha-93), G2 (DHL02) and, G6 (Gemmeiza-9), these were marked by high stability and productivity. The GGE biplot analysis showed genotypes (G15 (Misr1) and G4 (DHL07)) recorded the highest grain yield in E3 and E4, whereas genotype G18 (Sakha-93) was in E6. It also showed G19 (Pavone-76) was the best genotype due to being situated in the center of the concentric circles and due to its high-yield. The methods considered were compatible with the detection of promising wheat genotypes with high mean performance and outstanding phenotypic stability across various stresses and years.