The challenge with multifocus images lies in different regions being in focus across various shots, resulting in some areas appearing blurry while others are sharp. This issue is prevalent in fields such as medical imaging, remote sensing, and photography, where clear and detailed images are essential. This project introduces a novel approach to multifocus image fusion by integrating the Marr--Hildreth edge detection technique with Discrete Cosine Transform (DCT), Stationary Wavelet Transform (SWT), and Discrete Wavelet Transform (DWT). The Marr--Hildreth algorithm detects edges by identifying zero-crossings in the Laplacian of a Gaussian-blurred image, effectively highlighting areas with significant intensity changes. The proposed method was evaluated across four datasets--clocks, leaves, balloons, and bottles. Performance metrics such as Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Entropy indicated that this integrated approach significantly enhances image quality. By combining Marr--Hildreth edge detection with DCT, SWT, and DWT, the method improves image clarity and detail, offering a promising advancement in image fusion techniques. This innovative approach not only addresses multifocus image challenges but also paves the way for further developments in image fusion processes. The study suggests that enhanced image processing techniques can be applied across various fields requiring high-quality image fusion.