Genomic selection (GS) facilitates the rapid selection of superior genotypes and accelerates the breeding cycle. In this review, we discuss the history, principles, and basis of GS and genomic-enabled prediction (GP) as well as the genetics and statistical complexities of GP models, including genomic genotype×environment (G×E) interactions. We also examine the accuracy of GP models and methods for two cereal crops and two legume crops based on random cross-validation. GS applied to maize breeding has shown tangible genetic gains. Based on GP results, we speculate how GS in germplasm enhancement (i.e., prebreeding) programs could accelerate the flow of genes from gene bank accessions to elite lines. Recent advances in hyperspectral image technology could be combined with GS and pedigree-assisted breeding. In recent years, the global climate has changed, resulting in drastic fluctuations in rainfall patterns and increasing temperature. Sudden climate changes can cause significant economic losses to countries worldwide. Genetic improvement of several economically important crops during the 20th century using phenotypic, pedigree, and performance data was very successful. However, signs of grain yield stagnation in some crops, especially in drought-stressed and semi-arid regions, are evident. Genomic selection offers the opportunity to increase grain production in less time. International Maize and Wheat Improvement Center (CIMMYT) maize breeding research in Sub-Saharan Africa, India, and Mexico has shown that genomic selection can reduce the breeding interval cycle to at least half the conventional time and produces lines that, in hybrid combinations, significantly increase grain yield performance over that of commercial checks. Public and private investment in crop genomic selection research should increase to successfully develop in less time germplasm that is adapted to sudden climate change.