Forensic science increasingly relies on modern analytical chemistry to identify materials associated with crimes, determine their characteristics, and compare them with reference samples. Advances in analytical instruments and data processing techniques have improved analytical sensitivity and accuracy, allowing the examination of small and complex samples. Fourier-transform infrared spectroscopy (FTIR) and Raman spectroscopy are among the most important techniques used in forensic evidence analysis. They provide information about the molecular composition of materials and can be used to examine fibers, inks, paints, polymers, and certain types of biological evidence. Raman spectroscopy allows the analysis of very small samples, often without destroying the evidence. It can also be combined with microscopy to examine specific microscopic regions of a sample. Mass spectrometry (MS) plays a central role in forensic analysis because of its high sensitivity and ability to identify chemical compounds. Gas chromatography–mass spectrometry (GC-MS) is widely used to analyze drugs, poisons, solvents, and ignitable liquid residues. Liquid chromatography–mass spectrometry (LC-MS), meanwhile, enables the analysis of nonvolatile and thermally sensitive compounds, including pharmaceuticals, metabolites, and biological molecules. Advanced techniques have also emerged, including comprehensive two-dimensional gas chromatography (GC×GC) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). These techniques provide enhanced capabilities for separating and analyzing complex mixtures and examining the chemical distribution across evidence surfaces. Their applications include the examination of inks, paints, hair, fingerprints, trace materials, and other types of evidence. The increasing volume of data generated by analytical instruments has highlighted the importance of chemometrics, which uses mathematical and statistical methods to extract meaningful information from chemical data. Techniques such as principal component analysis (PCA), discriminant analysis, and other multivariate methods are used to distinguish samples and assess relationships between evidence items based on their chemical fingerprints. Artificial intelligence and machine learning represent an emerging direction in forensic analytical chemistry. They can support sample classification, recognition of spectral patterns, analysis of chromatographic data, and prediction of a material’s identity or possible origin. Artificial intelligence can also be integrated with FTIR, Raman spectroscopy, GC-MS, and LC-MS to improve the speed and accuracy of interpreting analytical results. Important applications of these techniques include the analysis of blood, body fluids, drugs, poisons, fibers, paints, glass, soil, inks, and fire debris. Combining multiple analytical techniques provides a more comprehensive chemical fingerprint and reduces reliance on a single analytical indicator. Despite these advances, forensic analytical chemistry faces challenges related to sample complexity, limited evidence quantities, possible contamination, and variations in measurement conditions. The use of artificial intelligence also requires reliable databases, diverse training data, independent model validation, and clearly established error rates, particularly when results have legal significance. The future of forensic evidence analysis is moving toward greater integration of spectroscopy, chromatography, mass spectrometry, chemometrics, and artificial intelligence. This integration is expected to support faster and more sensitive analytical systems capable of examining complex evidence. Human expertise, quality control, and scientific validation will remain essential to ensuring the reliability of forensic results.