Robotic　milling　is　becoming　widely　used　in　aerospace　and　auto　manufacturing　due　to　its　high　flexibility　and　strong　adaptability.　However,　the　practical　challenges　including　complex　and　time-consuming　robot　trajectory　planning,　insufficient　monitoring,　and　lacking　three-dimensional　visualization　limits　its　further　application.　To　address　these　challenges,　an　intelligent　monitoring　system　for　robotic　milling　process　based　on　transfer　learning　and　digital　twin　was　proposed　and　developed　in　this　paper.　Firstly,　the　fundamental　framework　of　this　system　was　conducted　based　on　a　five-dimensional　digital　twin　model　for　motion　simulation,　visualization,　and　tool　wear　prediction　during　the　robotic　milling　process.　Secondly,　the　parsing　algorithm　converting　NC　code　to　robot＇s　machining　trajectory　and　material　removal　algorithm　based　on　bounding　box　and　mesh　deformation　were　proposed　for　robotic　dynamic　milling　simulation.　Thirdly,　a　novel　transfer　learning　algorithm　named　CNN-LSTM-TrAdaBoost.R2　was　developed　by　integrating　CNN-LSTM　with　TrAdaBoost.R2　for　automated　feature　extraction　and　real-time　prediction　of　tool　wear.　Finally,　the　effective　and　accuracy　of　tool　wear　prediction　algorithm　is　verified　by　ablation　experiment　and　the　robotic　milling　simulation　is　validated　by　real　milling　experiment,　as　well.　The　results　indicate　that　the　proposed　monitoring　system　for　robotic　milling　process　demonstrates　great　virtual-real　mapping.　It　can　offer　new　insights　and　technical　support　for　constructing　sophisticated　digital　twin　frameworks　and　enhancing　operational　monitoring　in　manufacturing　systems.