Enhancing medical images is essential for improving both their visual quality and diagnostic accuracy. Medical images often suffer from issues such as low contrast, noise, and suboptimal resolution, which can hinder accurate medical evaluation. This research introduces a robust image enhancement framework designed to tackle these challenges by sharpening image clarity, emphasizing anatomical features, and retaining vital image details. A novel enhancement approach, referred to as CMT (Combination of Multi Technique), was developed using MATLAB. This approach integrates a custom-designed Logarithmic Enhancement method with traditional techniques such as Histogram Equalization and Sobel Edge Detection to leverage the strengths of each for improved intensity enhancement. Following enhancement, a CNN-based denoising model is applied to reduce residual noise. The proposed Logarithmic Enhancement method outperforms traditional logarithmic techniques in terms of intensity improvement. Overall, the framework demonstrates effective image enhancement capabilities, yielding high PSNR and low RMSE values.