With　the　rapid　advancement　of　urbanization,　the　proliferation　of　noise　sources　has　significantly　contributed　to　the　deterioration　of　the　urban　acoustic　environment.　Understanding　the　relationship　between　urban　planning　and　acoustic　quality　has　thus　become　an　urgent　priority.　The　study　investigates　this　relationship　by　utilizing　indicators　related　to　urban　construction,　land　use,　and　socio-economic　factors.　It　employs　multivariate　statistical　methods　and　supervised　learning　algorithms-including　Genetic　Algorithm-Back　Propagation　(GA-BP)　neural　networks,　Support　Vector　Regression　(SVR),　Least　Squares　Support　Vector　Machine　(LSSVM),　and　Extreme　Learning　Machine　(ELM)-to　conduct　regression-based　predictions　and　assess　the　potential　impacts　of　urban　development　on　acoustic　environment　quality.　Findings　showed　that:　(1)　Due　to　the　significant　spatial　heterogeneity　in　noise　indicators　across　different　cities,　optimizing　the　urban　acoustic　environment　requires　precise,　city-specific　strategies　to　ensure　efficient　resource　allocation　and　maximize　the　effectiveness　of　environmental　management.　(2)　Eleven　urban　construction　indicators-including　road　traffic　infrastructure,　land　use　types,　industrial　land,　built-up　areas,　residential　zones,　and　commercial　service　land-show　a　positive　correlation　with　environmental　noise.　Road　traffic　noise　is　positively　associated　with　the　extent　of　built-up　areas,　residential　land,　green　coverage　within　built-up　zones,　and　bare　land.　(3)　Stepwise　regression　analysis　reveals　divergent　effects　of　population　density:　it　negatively　correlates　with　road　traffic　noise　but　positively　correlates　with　regional　environmental　noise.　(4)　Among　the　four　supervised　learning　algorithms,　LSSVM　demonstrated　the　best　predictive　performance　for　road　traffic　noise,　achieving　the　lowest　MAE　(0.2728)　and　RMSE　(0.3186),　indicating　superior　accuracy　and　minimal　prediction　error.　This　study　offers　theoretical　insights　into　the　management　of　urban　acoustic　environments　and　provides　practical　recommendations　for　improving　urban　planning　and　mitigating　noise　pollution.