Infertility is a common problem affecting one in six couples and in 30% of infertile couples, the male factor is a major cause.A large number of genes are involved in spermatogenesis and a significant proportion of male infertility phenotypes are of genetic origin. Studies on infertility have so far primarily focused on chromosomal abnormalities and sequence variants in protein-coding genes and have identified a large number of disease-associated genes. However, it has been shown that a multitude of factors across various omics levels also contribute to infertility phenotypes. The complexity of male infertility has led to the understanding that an integrated, multi-omics analysis may be optimal for unravelling this disease. While there is a vast array of different factors across omics levels associated with infertility, the present review focuses on known factors from the genomics, epigenomics, transcriptomics, proteomics, metabolomics, glycomics, lipidomics, miRNomics, and integrated omics levels. These include: repeat expansions in AR, POLG, ATXN1, DMPK, and SHBG, multiple SNPs, copy number variants in the AZF region, disregulated miRNAs, altered H3K9 methylation, differential MTHFR, MEG3, PEG1, and LIT1 methylation, altered protamine ratios and protein hypo/hyperphosphorylation. This integrative review presents a step towards a multi-omics approach to understanding the complex etiology of male infertility. Currently only a few genetic factors, namely chromosomal abnormalities and Y chromosome microdeletions, are routinely tested in infertile men undergoing intracytoplasmic sperm injection. A multi-omics approach to understanding infertility phenotypes may yield a more holistic view of the disease and contribute to the development of improved screening methods and treatment options. Therefore, beside discovering as of yet unknown genetic causes of infertility, integrating multiple fields of study could yield valuable contributions to the understanding of disease development. Future multi-omics studies will enable to synthesise fragmented information and facilitate biomarker discovery and treatments in male infertility.