Water loss from underground pipeline leaks poses a major challenge to infrastructure, causing economic costs and service disruptions. This study aims to enhance leak detection and localization accuracy using acoustic signal processing techniques that combine Continuous Wavelet Transform (CWT) with automated wavelet optimization in buried ductile iron pipelines. Adaptive filtering, spectral analysis, and time-delay estimation were applied to acoustic emission data from pipe- line systems. To validate the approach, pipeline data collected using a modern correlator test on a 43-m pipeline showed the CWT method, advanced wavelet selection script, achieved 0% localization error for a leak at 5.7 m versus Fast Fourier Transform FFT-based analysis with 2.81% error. The dominant detected frequency (~247.71 Hz) aligned with the theoretical mode (245 Hz), confirming the method’s physical accuracy. The proposed method can improve leak detection accuracy by up to 100%, offering a scalable solution to reduce water loss and support sustainability goals in water utility networks.