Pharmaceutical analysis is a vital branch of analytical chemistry ensuring the safety, quality, and efficacy of active pharmaceutical ingredients and formulations as per ICH guidelines. It encompasses qualitative and quantitative evaluation using classical, spectroscopic, chromatographic, electrochemical, and biological methods. However, analysis of complex pharmaceutical, biological, environmental, and forensic matrices requires enhanced sensitivity, selectivity, and structural information, leading to the development of hyphenated techniques.

Hyphenated techniques combine a separation technique with a detection technique to provide more reliable data. This review focuses on liquid chromatography-mass spectrometry, a powerful hyphenated platform that synergizes high-performance liquid chromatography separation with mass spectrometric detection.

The development of LC-MS began in the late 1960s with liquid-introduction interfaces, progressed through direct liquid introduction in 1979, and was revolutionized with electrospray ionization in 1984 and its interface in 1985. Modern instrumentation comprises solvent delivery system, autosampler, chromatographic column, ionization source such as electrospray and atmospheric pressure chemical ionization, mass analyser such as quadrupole, time-of-flight and Orbitrap, and data system. Advanced variants include LC-MS/MS, LC-NMR, CE-MS, LC-MS-NMR, GC-MS/MS, and multidimensional LC×LC and GC×GC, which improve resolution of complex mixtures.

Currently, LC-MS is indispensable in drug substance and product analysis, drug discovery and absorption, distribution, metabolism, and excretion studies, pharmacokinetic and bioanalytical studies in plasma and urine, impurity profiling, stability and degradation studies, metabolomics, proteomics, lipidomic, biopharmaceutical analysis, biomarker discovery, food safety, environmental and forensic applications.

Recently, artificial intelligence and machine learning have been integrated with LC-MS for automated data preprocessing, peak detection, retention time prediction, spectral annotation, non-targeted analysis, biomarker discovery, and quality-control anomaly detection. While promising, AI integration requires validated datasets, interpretability, and regulatory acceptance.