Efficient compression of Electrocardiogram (ECG) signals is of considerable importance in wearable and remote healthcare systems, particularly in Wireless Body Area Networks (WBANs), where limitations in energy consumption, transmission bandwidth, and computational resources impose strict constraints on continuous physiological data monitoring. In this research, a novel ECG signal compression framework is proposed by integrating the Discrete Wavelet Transform (DWT) with the Bat Algorithm, a population-based metaheuristic optimization technique. In the proposed approach, the raw ECG signal is first decomposed into multiple frequency components in the wavelet domain, enabling an efficient representation of the signal in terms of a relatively small number of significant coefficients. Subsequently, the Bat Algorithm is employed to identify and select the optimal set of wavelet coefficients by maximizing the Peak Signal-to-Noise Ratio (PSNR) of the reconstructed signal. This optimization process allows the algorithm to preserve the coefficients carrying the most relevant morphological and diagnostic information while eliminating less significant components, thereby achieving effective data reduction without substantially degrading signal quality. The performance of the proposed method is evaluated using ECG recordings from the widely used MIT-BIH database. Experimental results demonstrate that the proposed DWT–Bat Algorithm framework achieves superior PSNR performance compared with conventional ECG compression techniques, indicating an improved trade-off between compression efficiency and reconstruction fidelity. The proposed approach therefore provides a promising solution for reducing the amount of transmitted ECG data while maintaining clinically relevant signal characteristics. Owing to its compression capability and potential for reduced communication overhead and energy consumption, the method can be considered suitable for real-time ECG monitoring applications in resource-constrained WBAN and wearable healthcare systems.