Single-cell analysis (SCA) improves the detection of cancer, the immune system, and chronic diseases from complicated biological processes. SCA techniques generate high-dimensional, innovative, and complex data, making traditional analysis difficult and impractical. In the different cell types, conventional cell sequencing methods have signal transformation and disease detection limitations. To overcome these challenges, various deep learning techniques (DL) have outperformed standard state-of-the-art computer algorithms in SCA techniques. This review discusses DL application in SCA and presents a detailed study on improving SCA data processing and analysis. Firstly, we introduced fundamental concepts and critical points of cell analysis techniques, which illustrate the application of SCA. Secondly, various effective DL strategies apply to SCA to analyze data and provide significant results from complex data sources. Finally, we explored DL as a future direction in SCA and highlighted new challenges and opportunities for the rapidly evolving field of single-cell omics.