In　conventional　spinal　surgeries,　mechanical　and　thermal　injuries　frequently　usually　arise　due　to　improper　handling,　giving　rise　to　a　range　of　complications　including　infection,　poor　wound　healing　and　bleeding.　Femtosecond　laser　ablation　offers　a　promising　approach　owing　to　high　precision　and　low　thermal　damage.　In　this　study,　an　intelligent　femtosecond　laser　drilling　method　of　human　spinal　bones　has　been　proposed,　and　a　machine　learning　method　has　been　employed　to　determine　the　optimal　laser　processing　window,　ensuring　high　-quality　outcomes.　A　neural　network　model　has　been　developed　to　predict　drilling　quality,　achieving　an　impressive　accuracy　rate　exceeding　98　%,　along　with　precision　and　recall　rates　of　100　%　and　92.86　%,　respectively.　To　further　monitor　the　process,　a　fiber　spectrometer　and　a　thermal　camera　has　been　employed　to　monitor　the　focal　status　and　bone　temperature　during　laser　processing　to　make　sure　the　drilling　is　in　a　focal　position　and　temperature　in　safe　range.　Subsequently,　the　drilling　efficiency　has　been　predicted　using　another　neural　network　model　within　high　-quality　processing　window　for　the　maximum　ablation　processing　parameter.　The　current　research　has　demonstrated　a　direct,　non-destructive　and　efficient　method　for　intelligent　laser　spinal　drilling.