Intercity　networks　are　interconnected　systems　of　cities　and　towns　that　collaborate　and　interact　through　social,　economic,　and　infrastructural　connections.　Intercity　networks　constantly　evolve,　and　analyzing　their　evolution　is　crucial　for　urban　planning　and　regional　cooperation.　Existing　intercity　network　simulation　methods　that　rely　on　flow　data　have　short-time　series　and　face　privacy　issues,　while　methods　based　on　statistical　data　suffer　from　data　gaps　in　certain　regions　and　are　updated　slowly.　Nighttime　Light　(NTL)　data　offer　a　valuable　alternative　due　to　their　advantages　in　time-series　accessibility,　rapid　updating,　and　broad　coverage.　However,　current　studies　based　on　NTL　data　are　limited　to　regional　scales,　which　restricts　their　applicability　for　comprehensive,　large-scale,　and　historical　analyses　of　urban　development.　This　study　uses　machine　learning　models　to　simulate　China＇s　intercity　population　flow　networks,　using　intercity　population　flow　data　from　Baidu　as　the　target　variable　and　features　extracted　from　NTL,　land　cover,　and　road　data　as　input　variables.　Three　machine　learning　models,　including　Extreme　Gradient　Boosting　(XGBoost),　Random　Forest,　and　Light　Gradient　Boosting　Machine,　were　trained　and　tested　on　data　from　2020　to　2021.　The　XGBoost　model　achieved　R2　values　of　0.82　on　the　validation　set　and　0.77　on　the　test　set　and　was　selected　as　the　optimal　model　for　constructing　intercity　population　flow　networks　in　China　for　2012,　2017,　and　2022.　The　evolution　of　the　intercity　population　flow　network　was　examined　from　both　spatial　structure　and　city　centrality　perspectives.　The　findings　revealed　a　shift　in　China＇s　intercity　population　flows　spatial　structure　from　a　multipolar　pattern　to　a　combination　of　multipolar　and　rhombus-shaped　patterns.　From　2012　to　2022,　China＇s　largest　cities　first　developed　and　then　drove　the　development　of　neighboring　cities.　This　study　introduces　a　novel　method　for　intercity　population　flow　network　simulation　on　a　large　scale,　offering　valuable　insights　for　urban　planning　and　strategic　decision-making.