Global wheat improvement with state-of-the-art genomic technologies for increasing the rates of genetic gain, understanding the genetic basis of key traits and fingerprinting of germplasms is critical for sustaining wheat production. Since, genomic selection is a promising technology that could facilitate accurate selections, reduce the cycle time, and subsequently accelerate genetic gains, we assessed the genomic predictabilities of 35 key trait-environment/pathogen race combinations evaluated globally in 3,485 wheat breeding lines from the International wheat and maize improvement center. Our results indicate that in cross validations, traits like grain color, stem rust (SR) seedling and field resistance, mixing time, Alveograph, loaf volume and protein content, have high genomic predictabilities (0.60-0.85), while traits like phenology and grain yield (GY) have moderate predictabilities (0.42-0.59). We also evaluated the ability of historic training populations to predict different traits in across-panel genomic predictions. Here, several end-use quality and disease resistance traits had only an average of 0.07 decrease in accuracies from the corresponding cross-validation accuracies, it is evident that GS could be a game-changer for those, to scale-up phenotyping in large unphenotyped populations and making precise selections. But, GY in optimally irrigated, heat and drought-stressed environments had an average decrease of 0.20 from the corresponding cross-validation accuracies, indicating that historical single-environment training populations might not be ideal for predicting a trait like GY that is subject to high genotype x environment interactions. Furthermore, we dissected the genetic architecture of all the traits using a genome-wide association mapping approach and fingerprinted 44,624 lines for trait-linked markers, which will be a phenomenal resource to the global wheat community for enhancing productivity, stressresilience and quality through genomics.