An echocardiogram is a very vital process that provides imaging information on heart diseases diagnosis and follow up. Among the leading causes of death all over the world are heart diseases, and therefore quality images are necessary in the correct medical analysis, though noise and distortion tend to compromise the quality of images used in diagnoses. In this paper, an ML based innovative method has been discovered on the combination of Mask R-CNN with the Radial Basis Function Support Vector Machine (RBF-SSVM) and demonstrates better quality improvement and correct classification of quality echocardiogram images and improves the image recovery quality of noise removal through high-cardiogramimage-classifying accuracies and will be very helpful in application to the research topic of clinical application. The outcome of the experiments is such that 97.8 percent accurate in the classification of the experimental results has been attained, which represents the effectiveness of this approach to addressing the issues of echocardiogram imaging. The article is a breakthrough in the use of ML to enhance cardiac diagnostics

Echocardiogram Imaging, Cardiac Diagnostics, Machine Learning, Mask R-CNN, RBF-SSVM, Speckle Noise Suppression