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The integrity and authenticity of Chinese medicinal materials are essential for ensuring the efficacy of medicines and safety of patients. Angelicae Sinensis Radix (ASR), a highly valued traditional Chinese medicine (TCM), is often substituted or mixed with lower-cost alternatives. In this study, a novel method was proposed for the quantification of adulterated ASR using near-infrared (NIR) spectroscopy combined with chemometrics. Binary and ternary adulterated ASR samples were prepared by mixing ASR with Angelicae Pubescentis Radix (APR) and Chuanxiong Rhizoma (CR), and their NIR spectra were measured in the range of 1000-1800 nm. Sample subsets were initially selected using Monte Carlo (MC) sampling, followed by the application of whale optimization algorithm (WOA) to select variables and establish an extreme learning machine (ELM) model, referred to as MC-WOA-ELM. The iteration number and percentage of training subsets for MC-WOA-ELM were optimized to further improve the predictive performance of model. The predictive ability of the proposed method was compared with partial least squares (PLS), ELM and WOA-ELM for both binary and ternary adulterated ASR samples. The results indicate that MC-WOA-ELM achieves the highest prediction accuracy. Therefore, the proposed method is rapid and intelligent for the quantification of adulterated ASR samples.