Genomics creates large databases for the discovery, study and production of new therapeutics worldwide. It would not be impossible to imagine that 3 billion base pairs comprising the humanoid genetic makeup may now be studied to find genetic differences within the population by artificial intelligence. Large pharmaceutical firms such as Astra Zeneca are aiming to research up to 2 million genomes by 2026 and review vast quantities of patient data points from their clinical drug trials. AI will be used in genomics for multiple omics experiments, such as transcriptomics, as we introduce more instruments. AI is increasingly being used by healthcare firms in accordance with HEOR (Health Economics Outcome Research), i.e. In order to help classify possible clinically important genes, AI is used to combine data produced from genomic studies with analysis from science literature. Machine learning today plays an integral role in the development of the genomics industry. In this paper, we set out to explore the uses of genomics machine learning to help market leaders consider existing and evolving developments in the field. We have discussed history terms and distilled perspectives from various study. Current applications of machine learning in gene technology boost up future applications of genomics machine learning.