This review critically examines the methods of quantitative mass spectrometry in proteomics, focusing on their individual merits and challenges. It highlights the increasing popularity of mass spectrometry-based quantification methods over classical methods like differential gel or blot staining. These methods often employ stable isotope labeling to create specific mass tags that can be recognized by mass spectrometers, providing both identification and quantification. Label-free quantification approaches, which correlate mass spectrometric signals with protein quantity, are also discussed. The review covers various labeling strategies, including metabolic labeling, chemical and enzymatic labeling, and absolute quantification using internal standards. It discusses the advantages and limitations of each method, emphasizing the importance of data quality and the need for statistical analysis to interpret quantitative proteomic data accurately. The review concludes by addressing the challenges in analyzing complex proteomic data and the importance of robust data processing and statistical methods to ensure reliable results.This review critically examines the methods of quantitative mass spectrometry in proteomics, focusing on their individual merits and challenges. It highlights the increasing popularity of mass spectrometry-based quantification methods over classical methods like differential gel or blot staining. These methods often employ stable isotope labeling to create specific mass tags that can be recognized by mass spectrometers, providing both identification and quantification. Label-free quantification approaches, which correlate mass spectrometric signals with protein quantity, are also discussed. The review covers various labeling strategies, including metabolic labeling, chemical and enzymatic labeling, and absolute quantification using internal standards. It discusses the advantages and limitations of each method, emphasizing the importance of data quality and the need for statistical analysis to interpret quantitative proteomic data accurately. The review concludes by addressing the challenges in analyzing complex proteomic data and the importance of robust data processing and statistical methods to ensure reliable results.