Laser-Induced Breakdown Spectroscopy (LIBS) has emerged in recent years as a widely recognized atomic spectroscopy technique. In addition to analyzing light-matter interaction processes, this technology can also detect some representative molecular fragment bands. Thanks to its unique advantages, such as convenient detection, simultaneous multi-element analysis, real-time feedback, and controlled costs, LIBS shows great promise in the field of clinical medicine. Meanwhile, cancer, being one of the most challenging problems in twenty-first-century healthcare, urgently requires rapid and efficient diagnostic mechanisms to guide treatment plans and facilitate foundational research, including the analysis of disease causes. This chapter provides a comprehensive analysis and discussion of cancer diagnosis based on LIBS technology, covering aspects such as sample types, detection theoretical foundations, data analysis methods, and artificial intelligence techniques. It aims to serve as a reference for young students and researchers in related fields. In terms of sample types, cancer detection encompasses soft tissues, hard tissues, and biofluids. Regarding theoretical foundations, the focus lies on the pathological mechanisms and emerging theoretical dimensions, such as polarization information. In the context of data analysis, the emphasis is on the unique demands of artificial intelligence analysis methods compared to other scenarios.