Surface　nanostructuring　could　enhance　surface　properties　such　as　strength,　self-cleaning,　anti-fog　and　anti-bacterial　properties.　Femtosecond　laser-induced　periodic　surface　structures　(LIPSS)　is　a　nanoscale　structure　created　with　laser　technique.　However,　its　quality　is　significantly　influenced　by　the　complicated　interrelationship　between　the　various　laser　processing　and　material　parameters.　Hitherto　the　selection　of　the　appropriate　laser　parameters　mainly　depends　on　personal　experience　in　conjunction　with　many　time-consuming　experimental　trials.　To　have　a　simple,　fast,　and　intelligent　process,　a　hybrid　machine　learning　method　is　proposed　to　determine　the　optimized　processing　window　for　femtosecond　laser-induced　nanostructures.　Firstly,　k-means　clustering　method　was　applied　to　automatically　classify　the　laser-induced　nanostructures　into　good　and　bad　quality　classes.　Before　clustering,　dimensionality　reduction　methods　were　applied　to　reduce　the　high　dimension　of　image　data　and　to　extract　features.　Different　dimensionality　reduction　methods　including　principal　component　analysis　(PCA),　local　linear　embedding　(LLE),　t-random　adjacent　embedding　(t-SNE)　and　transfer　learning　were　explored.　Transfer　learning　showed　a　much　better　result　compared　with　other　dimensionality　reduction　methods.　Transfer　learning　VGG19　model　achieved　the　highest　accuracy　of　90.6　%.　After　clustering,　the　image　was　labelled　as　good　and　bad　clusters　accordingly.　The　labeled　image　was　trained　using　artificial　neural　network　(ANN),　random　forest　(RF),　decision　tree　(DT),　support　vector　machine　(SVM),　k-nearest　neighbors　(KNN)　and　Naive　Bayesian　Classifier　(NBC)　algorithms　for　the　prediction　of　laser　processing　results.　The　results　show　that　DT　gives　the　best　accuracy　of　96.7　%.　Finally,　an　optimal　laser　processing　window　for　femtosecond　laser-induced　nanostructures　was　determined.