The　critical　role　of　high-quality　urban　development　and　scientific　land　use　in　leveraging　big　data　for　air　quality　enhancement　is　paramount.　The　application　of　machine　learning　for　causal　inferences　in　research　related　to　big　data　development　and　air　pollution　presents　considerable　potential.　This　study　employs　a　double　machine　learning　model　to　explore　the　impact　of　big　data　development　on　the　PM2.5　concentration　in　277　prefecture-level　cities　across　China.　This　analysis　is　grounded　in　the　quasi-natural　experiment　named　the　National　Big　Data　Comprehensive　Pilot　Zone.　The　findings　reveal　a　significant　inverse　relationship　between　big　data　development　and　PM2.5　levels,　with　a　correlation　coefficient　of　-0.0149,　a　result　consistently　supported　by　various　robustness　checks.　Further　mechanism　analyses　elucidate　that　big　data　development　markedly　diminishes　PM2.5　levels　through　the　avenues　of　enhanced　urban　development　and　land　use　planning.　The　examination　of　heterogeneity　underscores　big　data＇s　suppressive　effect　on　PM2.5　levels　across　central,　eastern,　and　western　regions,　as　well　as　in　both　resource-dependent　and　non-resource-dependent　cities,　albeit　with　varying　degrees　of　significance.　This　study　offers　policy　recommendations　for　the　formulation　and　execution　of　big　data　policies,　emphasizing　the　importance　of　acknowledging　local　variances　and　the　structural　nuances　of　urban　economies.