Machine Learning (ML) methodologies have become a worthy choice for X-ray screening. X-ray imaging is a method used for the identification of bone fractures. But every so often, the dimensions and locations of a fracture are detected incorrectly. Therefore, this paper aims to design a system that would accurately detect and classify fractured and non-fractured bone images. This proposed system comprises four principal phases. The first is the image acquisition phase, in which few input images are acquired from the X-ray machine, and few input images are collected from the imaging centre. The second is a pre-processing phase to see their informative areas like location, edges and shape. So, the wavelet transform method helps to compress the images to store and remove noises. The third is the feature extracting phase; the Harris corner detection technique is used to locate the broken points as corner features and intensifies the quality of the X-ray images. Before applying the Harris corner algorithm, the image sharpening method was used. Then images are equipped to be fed into a fourth phase, i.e. classification phase; two techniques are used in this phase: Support Vector Machine (SVM) and Error Backpropagation Neural Network (EBP-NN). Classification by SVM and EBP-NN both methods are tested on various fractured and non-fractured bone images. Finally, found that classification done by the SVM technique is more accurate than EBP-NN. The classification by the SVM approach has higher efficiency and a higher classification rate of 94%, which is 2% more than the EBP-NN technique.