Abstract: Cellular heterogeneity is a fundamental characteristic of biological systems, influencing development, physiological function, and disease progression. While single-cell transcriptomics has revolutionized our understanding of gene expression, mRNA levels often correlate poorly with protein abundance due to post-transcriptional regulation. Consequently, single-cell proteomics (SCP) has emerged as a critical frontier, aiming to characterize the functional molecular executors of life at single-cell resolution. However, SCP faces significant technical hurdles compared to nucleic acid sequencing, primarily due to the ultra-low abundance of proteins in single cells (sub-nanogram levels), the inability to amplify proteins, and the high dynamic range of the proteome. This review systematically synthesized the rapid technological evolution across the entire SCP workflow. 1) Single-cell isolation: strategies ranging from fluorescence-activated cell sorting (FACS) to laser capture microdissection (LCM) for spatial context were discussed, highlighting the transition toward automated, image-guided dispensing systems (e.g., CellenONE) that ensure high cell viability and accurate isolation. 2) Sample preparation: the review emphasized the shift toward “miniaturization” and “integration” to mitigate surface adsorption and sample loss. The innovative nanoliter-scale processing platforms were introduced in this study, including droplet-based microfluidics (SODA, PiSPA), nanowell chips (nanoPOTS), and all-in-one devices (ProteoCHIP, Chip-Tip). These platforms have successfully reduced reaction volumes to the nanoliters scale, significantly enhancing peptide recovery. 3) Chromatography and mass spectrometry: the impact of narrow-bore capillary columns and robust low-flow LC systems (e.g., Evosep One) on sensitivity was analyzed. Furthermore, the integration of advanced ion mobility technologies (TIMS/PASEF) and high-field Orbitrap analyzers (e.g., Orbitrap Astral) have revolutionized detection limits, enabling the identification of over 5 000 proteins from single cells. 4) Data acquisition and analysis: the transition from data-dependent acquisition (DDA) to data-independent acquisition (DIA), particularly direct-DIA (library-free), is highlighted as a standard for improving data completeness and reproducibility. Additionally, the emergence of deep learning algorithms (e.g., scPROTEIN) and specialized databases (SingPro, SPDB) that address the challenges of high missing values and batch effects was discussed. The review lighlighted the critical development of spatial proteomics (e.g., deep visual proteomics), which maps protein expression to tissue architecture, resolving the “loss of position memory” inherent in dissociated samples. This work further showcases the application of SCP in decoding macrophage heterogeneity, tracing stem cell differentiation trajectories, and elucidating drug resistance mechanisms in cancer. Despite remarkable progress, challenges remain regarding throughput, depth of coverage, and multi-omics integration. Future developments must focus on further automating sample preparation, enhancing the sensitivity of ionization sources, and integrating SCP with transcriptomics and metabolomics. Ultimately, standardization of workflows and data analysis is essential to translate SCP from an exploratory research tool into a robust clinical platform for precision medicine and biomarker discovery.