This paper evaluates various wavelet-based techniques and multi-level decomposition strategies for efficient image compression, emphasizing the performance of four prominent wavelet families: Haar, Daubechies, Biorthogonal, and Meyer. Image compression aims to reduce the data required to represent an image while maintaining its visual quality, a crucial aspect of digital imaging. Wavelet-based compression algorithms are widely recognized for achieving high compression ratios with minimal loss of information, making them ideal for this purpose. The paper systematically applies each wavelet type to a set of standard images, performing compression at different decomposition levels to analyze the impact on both compression efficiency and image quality. Haar wavelets, noted for their simplicity and computational efficiency, are used as a baseline. Daubechies and Biorthogonal wavelets are evaluated for their ability to provide more detailed reconstructions, while Meyer wavelets are examined for their smoothness and compact support, which contribute to image fidelity preservation during compression. The study assesses performance using metrics such as Mean Square Error (MSE), Maximum Error (ME), L2 Norm ratio, Peak Signal-to-Noise Ratio (PSNR), Bits Per Pixel (BPP), and Compression Ratio (CR). The results indicate that multi-level decomposition, combined with appropriate wavelet selection, significantly enhances image compression performance. These findings offer valuable insights for selecting optimal wavelet approaches in image processing applications.