Pipelines　play　a　crucial　role　in　today＇s　infrastructure.　However,　they　are　easily　affected　by　environmental　conditions.　Therefore,　regular　inspections　are　essential　to　ensure　the　integrity　of　pipelines.　This　study　introduces　a　new　approach　that　combines　variation　mode　decomposition　(VMD)　and　empirical　wavelet　transform　(EWT)　with　continuous　wavelet　transform　(CWT)　for　acoustic　data　to　improve　the　signal-to-noise　ratio　(SNR)　and　accurately　detect　pipeline　leakage　location　in　the　time　domain.　The　acoustic　data　was　gathered　in　three　distinct　scenarios　inside　an　open　environment,　where　external　noise　considerably　influenced　the　original　signal.VMD　and　EWT　are　employed　to　enhance　the　signal-to-noise　ratio　(SNR).　VMD　efficiently　distinguishes　high-frequency　noise　and　assures　distinct　mode　separation,　whilst　EWT　delivers　precise　temporal　localization　for　non-stationary　signals.　Based　on　the　results　of　VMD　and　EWT,　certain　IMFs　were　selected　for　further　study　and　SNR　is　computed　among　the　respective　chosen　IMFs　and　original　signals　to　measure　the　improvement.　Subsequently,　the　CWT　was　utilized　on　the　chosen　IMF　for　feature　extraction,　effectively　finding　leakage　locations　in　the　time　domain.　This　study　illustrates　the　feasibility　of　successfully　mitigating　noise　and　extracting　signals　in　open　and　noisy　environments,　thus　enhancing　the　accuracy　of　leak　detection.　The　results　establish　a　robust　basis　for　subsequent　investigations　in　pipeline　leak　detection　and　demonstrate　the　applicability　of　advanced　signal　processing　approach　to　real-time　leak　detection　systems.