Occupational chronic lead poisoning is a gradually developing disease.It is easy to accumulate lead and develop into severe lead poisoning due to atypical early symptoms, which seriously threaten the health and quality of life of occupational groups. Mainstream analytical techniques have problems, such as requiring high precision but complicated and time-consuming operations, or being easy to operate but having poor applicability, which cannot achieve rapid in-situ detection. Laser-induced breakdown spectroscopy (LIBS), a new detection method, has demonstrated great potential and promising applications in the field of elemental analysis. In this paper, the feasibility of rapidly diagnosing occupational chronic lead poisoning is demonstrated using LIBS technology combined with machine learning algorithms. The whole blood sample preparation method has been optimized, and it is proposed that ultrasonic treatmentcan make the whole blood matrix more uniform, thereby alleviating the sample fragmentation problem when the laser is applied to dry blood. A glass slide is the most suitable substrate type compared to filter paper, graphite, and boric acid substrates. The effects of various LIBS experimental parameters on the signal intensity and signal-to-background ratio of the characteristic spectral lines of the element lead (Pb) were investigated. Simulated blood LIBS data for different types of occupational chronic lead poisoning were collected, and data dimensionality reduction was achieved by extracting features using principal component analysis (PCA). A 10-fold cross-validation and support vector machine (SVM) and back-propagation neural network (BPNN) were used to construct a diagnostic model for the chronic lead poisoning category. The recognition accuracies of both models reached more than 90%, which solved the problem of weak spectral difference of Pb elements in low concentration, which was difficult to be distinguished by traditional methods, and the BPNN model showed excellent diagnostic effect, with the classification accuracy and precision rate of 95.56% and 96.08%, respectively. The results demonstrate that machine learning-based LIBS technology can facilitate the timely detection of lead elemental excess in whole blood, providing an auxiliary method for the rapid and accurate screening of blood lead abnormalities, and supplementing the clinical basis for the diagnosis of occupational chronic lead poisoning.