In the forthcoming era of climate change and degradation of culturable land, there is an urgent need to secure global food supply in a sustainable manner. unraveling the genetic mysteries underlying interactions between functional genetic and metabolic networks through fostering the use of high-throughput -omic technologies can serve as a valuable tool towards characterizing plant phenotypic diversity and plasticity, to mitigate current threats of the climate change scenario on agriculture. Recently, a colossal number of -omic studies, including genomics, transcriptomics, proteomics, metabolomics, epigenomics and metagenomics, have enabled the identification of genes, proteins, and metabolites, that are related to desirable phenotypes, explaining the “holo-genetic” basis of agriculturally important traits, especially under resource-limiting environments. Undoubtedly, the integration of such big datasets with machine learning is highly demanding, mainly due to the lack of universal protocols to predict gene models or networks that govern various key traits. Among other important plant species contributing to food production, potato (Solanum tuberosum L.) represents one of top crop species worldwide, in terms of nutrition contribution, yielding capacity, and as a component of diverse cropping systems, especially for the developing counties. The potato genome is highly heterozygous as a result of self-incompatibility of the diploid potato species, suffering acute inbreeding depression. In this review, we discuss recent developments of high-throughput genomic technologies, as a useful tool for the selection of potato germplasm with improved nutritional value and quality traits.