Traditional Chinese medicine (TCM) has garnered significant global interest owing to its multi-component and multi-target theoretical framework and extensive therapeutic efficacy. However, the identification of quality markers (Q-markers) remains a formidable challenge in TCM. Hence, this study aimed to integrate network pharmacology and chemometrics to identify Q-markers in Chinese patent medicine, with a focus on Huo-Luo-San (HLS) as a case study. HLS, a widely used powdered Chinese patent medicine in China, comprises a complex formula of 10 herbs, initially formulated during the Qing dynasty for treating fractures. Initially, 13 components, chlorogenic acid, typhaneoside, isorhamnetin-3-O-neohesperidoside, cynaroside, notoginsenoside R1, ginsenoside Rg1, baicalin, berberine hydrochloride, ginsenoside Rb1, dehydrocostus lactone, dioscin, imperatorin, and costunolide, were selected as phytochemical markers for each herb based on the Chinese Pharmacopoeia (2020 version), forming the “Herbs-Compounds-targets” network of HLS using network pharmacology. Subsequently, employing network pharmacology, the 13 HLS components were quantified using UPLC-QqQ-MS. Chromatographic conditions were optimized on a Waters Cortecs C18 column (2.1 mm × 100 mm, 1.6 μm) with a gradient elution comprising 0.1% formic acid in water and acetonitrile. Analyte detection was performed in the multiple-reaction monitoring mode, and the method underwent validation for linearity, detection limit, precision, repeatability, stability, and accuracy. The validated method was then utilized to analyze the 13 components in 15 batches of HLS samples. Chemometric techniques, including hierarchical cluster analysis, principal component analysis, orthogonal partial least squares projection discriminant analysis, and box map analyses, were subsequently employed to identify the Q-markers. Ultimately, six components, baicalin, notoginsenoside R1, berberine hydrochloride, dioscin, imperatorin, and chlorogenic acid, were selected as Q-markers for HLS. The integration of network pharmacology with chemometrics represented a novel approach for selecting Q-markers in Chinese patent medicine.