Biotechnology advancements are increasingly leveraging big data stored in databases, requiring researchers to effectively combine their contributions with the vast volumes of data in these databases for significant progress. Big data and artificial intelligence are transforming biotechnology, enabling data integration, exploitation, and process optimization. In particular, the speed at which data from omics technologies genomics, epigenomics, transcriptomics, proteomics, metabolomics, and pharmacogenomics are being gathered is opening up new avenues for health care advancements. The fast growth of deep learning coupled with the massive and ever-increasing amount of available data presents several prospects for advances in the fusion and analysis of complex and diverse data kinds. Deep learning approaches for increased performance accuracy have been developed as a result of big data breakthroughs in genomic research. Large genetic datasets can only be analyzed using these methods, strengthening the case for classifying genomic sciences as “big data” fields. Scientific progress in deep learning techniques will revitalize the field of genomics research. Deep learning is a large area that encompasses many different strategies, each with pros and cons for integrating numerous omics. Deep learning approaches are being utilized in genomic research, addressing the limitations of existing methods and paving the way for the development of improved genomic task architectures. This article gives an overview of the connections between big data in genomics using deep learning approaches.