Pneumatic　valve-controlled　micro-droplet　generation　is　a　printing　technique　that　has　potential　applications　in　many　fields,　especially　in　the　field　of　biomedical　printing.　The　droplet　generation　is　controlled　by　a　solenoid　valve　being　briefly　turned　on,　so　that　high　pressure　gas　enters　the　liquid　reservoir,　forming　a　gas　pressure　pulse　P(t),　forcing　the　liquid　out　through　a　tiny　nozzle　to　form　a　micro-droplet.　Under　the　typical　working　conditions,　P(t)　is　not　consistent.　Since　P(t)　is　highly　correlated　with　the　micro-droplet　ejection　state,　the　inconsistency　of　P(t)　results　in　fluctuation　of　ejection　state.　For　each　injection,　the　P(t)　is　acquired　by　a　high　speed　pressure　sensor,　and　the　ejection　state　is　obtained　by　machine　vision　processing.　A　machine　learning　method　based　on　BP　neural　network　is　used　to　establish　a　prediction　model　with　P(t)　as　the　input　and　the　droplet　ejection　state　as　the　output.　Experiments　show　that　a　BP　neural　network　with　only　a　single　hidden　layer　and　two　neurons　can　accurately　predict　the　number　of　droplets　with　an　accuracy　higher　than　99%.　Another　experiment　shows　that　a　more　complex　double　hidden　layer　BP　neural　network　can　improve　the　prediction　accuracy　for　the　position　of　droplets　after　a　certain　time　delay.　In　summary,　through　pressure　pulse　P(t),　the　predictive　model　established　by　the　machine　learning　method　can　effectively　predict　the　micro-droplet　ejection　state.　This　technique　may　be　used　for　real　time　monitoring　and　control　of　the　pneumatic　valve-controlled　micro-droplet　generator.