In the multi-omics era, single-cell analysis methods have flourished, driven by the development of bioinformatics approaches, deep learning algorithms, diversification of molecular barcoding methods, and breakthroughs in emerging sequencing technologies. The single-cell analysis methods unveil the hidden diversity within seemingly uniform populations, unlocking a deeper understanding of molecular functions at the single-cell level and revealing the distinct characteristics and roles of individual cells within complex biological processes. This chapter briefly introduces single-cell technologies and then an overview of current methods in single-cell bioinformatics analysis and detailed explanations of their respective methodologies. The joint analysis of multi-omics technologies such as the genome, epigenome, transcriptome, proteome, and metabolome from single cells currently transforms our understanding of cell and developmental biology by uncovering the molecular hierarchy of the different “-omics” layers at the single-cell level and spatially. This chapter also emphasizes the advances in the rapidly developing field of single-cell and spatial multi-omics technologies. Moreover, the necessity of incorporating third-generation long-read sequencing technologies in single-cell research was emphasized, providing insights into their specific applications. Subsequently, the transformation of the single-cell genomic field by algorithmic advancements in machine and deep learning is discussed, and recently developed machine learning tools applied to real-world examples are presented, elucidating their practical applications. Altogether, our work offers insights into recent advancements in single-cell genomic technologies, examining both experimental methodologies and bioinformatics aspects.