Single-cell analysis has emerged as a powerful tool in molecular biology, offering unprecedented resolution into the complexities and heterogeneity of biological systems at the cellular level. This technique surpasses traditional bulk analysis methods, which often obscure cellular diversity by analyzing populations of cells as a homogeneous entity. Single-cell analysis is revolutionizing our understanding of various biological processes and disease mechanisms by providing insights previously unattainable. As technological and computational methods continue to evolve, single-cell analysis is expected to experience a further expansion in its capabilities and applications, leading to breakthroughs in precision medicine and a deeper understanding of biological processes. The goal of this review is to provide a brief overview of how this technique has evolved, tracing its development from rudimentary genetic analytical tools to its current state as a highly sophisticated methodology. It includes a thorough summary of the methods and instruments used in single-cell analysis, particularly via ribonucleic acid (RNA) sequencing, the complexities of data analysis, benefits and drawbacks, and finally its clinical applications. The review will assist researchers in choosing and putting into practice the best methodologies for their research requirements.