Bone fracturesBone fracture are common in humans and can happen from a minor mishap or from extreme pressure being placed on the bone. Because of this, the right diagnosis of a fragmented femur is essential in the therapeutic area. Radiograph are used in this work to examine bone fracturesBone fracture. This paper attempts to develop an approach based on the image processingImage processing that can use information from x-ray images to classify bone fracturesBone fracture quickly and reliably. The goal of this present investigation initiative is to progress an efficient image processingImage processing system that uses data from an x-ray imagery to classify cracks promptly and reliably in bones. Prompt and precise evaluation of bone fracturesBone fracture is crucial for effective treatment, as fractures can result from a variety of illnesses and injuries. Numerous machine learningMachine learning methods have been especially developed for bone fractureBone fracture detectionFracture detection, including Naïve Bayes, Decision TreeDecision Tree (DT), Nearest Neighbors, Random ForestRandom Forest (RF), and SVMSupport Vector Machine (SVM). The study utilized a range of algorithms, including Naïve Bayes, Decision TreeDecision Tree (DT), Nearest Neighbors, Random ForestRandom Forest (RF), and SVMSupport Vector Machine (SVM), whose accuracy measures ranged from 0.64 to 0.92. This study’s SVMSupport Vector Machine (SVM) accuracy was shown to be the greatest statistically, outperforming the majority of the analyzed studies.