Clubroot, caused by Plasmodiophora brassicae, is a destructive disease of Chinese cabbage (Brassica rapa ssp. pekinensis) at all growing stages. Early detection of the disease is essential to mitigate the impact of clubroot. Here, we established an optimal algorithm for multispectral imaging combined with machine learning to detect leaf responses of highly susceptible cultivar YoulvNo.3 at different day after inoculation (DAI). Spectral data at 19 wavelengths were collected from leaf multispectral images, and key characteristic wavelengths were further extracted. Principal Component Analysis (PCA) revealed a clear separation between healthy and infected samples at 11 DAI. Four classification algorithms, including Random Forest (RF), Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM) and Extreme Learning Machine (ELM), were employed to construct early detection model for clubroot. SVM achieved over 81% accuracy with full-spectrum data, while ELM based on characteristic wavelengths provided the best performance, accuracy exceeding 84%. Stratified five-fold cross-validation was used to validate the optimal model. An average accuracy of 83.79% (±1.04%) and macro-averaged F1-score of 82.13% (±1.12%) across validation folds were obtained, confirming stable performance. Our findings, for the first time, identified detectable spectral differences between the healthy and infected plants at 11 DAI using leaf multispectral combined with machine learning, providing a potential application for early detection of clubroot and timely control in Chinese cabbage.