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Simultaneous determination of multiple organic compounds in complex sample matrices remains a significant analytical challenge due to the diverse chemical properties and low concentration levels of target analytes. This review comprehensively evaluates current sample preparation techniques applied in multi-residue analysis coupled with chromatographic methods such as GC-MS/MS and LC-HRMS. Techniques including QuEChERS, solid-phase extraction (SPE), dispersive SPE, and solid-phase microextraction (SPME) demonstrate extraction recoveries ranging from 70% to 95% for various compound classes, with detection limits often reaching low ng/L or ng/g levels. The choice of sample preparation method is critically influenced by analyte polarity, solubility, and matrix complexity, with over 50% of reported studies focusing on food and environmental water samples. Furthermore, this review highlights the growing potential of artificial intelligence (AI) in enhancing chromatographic data interpretation and compound identification, though emphasizes that effective sample preparation remains foundational for AI-driven analysis. The integration of optimized sample preparation workflows with AI-assisted data processing promises to improve throughput, accuracy, and robustness in multi-residue analysis, supporting regulatory compliance and environmental monitoring efforts worldwide.