Respiration is an important index to evaluate the life status of organisms, thus monitoring respiration is of great significance. This study proposes a new methodology for non-invasive and real-time monitoring of organismal respiration based on laser-induced breakdown spectroscopy (LIBS). The study comprises two main components: dynamic monitoring and static analysis. In the dynamic monitoring section, LIBS is used to continuously detect and analyze multiple elements (C, H, O, and N) exhaled steadily by participants, with the carbon signal showing the closest correlation to the respiratory cycle. Fast Fourier Transform (FFT) is then applied to the carbon signal to achieve frequency domain analysis. Furthermore, it is also found that the presence of organismal respiration should be inferred from multiple element signals, rather than relying solely on the presence of carbon signal. In the static analysis, linear discriminant analysis (LDA) and backpropagation artificial neural networks (BP-ANN) are employed after LIBS measurement indicating the traceability of respiration sources is possible. This study offers a novel perspective on respiratory monitoring, demonstrating the potential of this method for real-time, convenient, and cost-effective respiration monitoring.