Bone fracture detection is a critical process in medical field. The early diagnosis leads to successful treatment, identifying bone fractures in medical imaging became very important to receive proper diagnosis. This research highlights new method of fracture diagnosis from X-Ray images based on sophisticated machine learning algorithms. This research uses a large dataset of annotated X-ray images containing different types of fractures. They can be likened to the machine learning software that automatically identify patterns in an image. This research is done to develop a smart, fully-automatic, and error-free mechanism which enables an AI assistant to scan X-ray images in order to be able to diagnose bone-related issues, like fractures. It uses the integration of multiple high-level machine learning (ML) quality techniques, Convolutional Neural Networks (CNN) and Mobile Net technologies. An X-ray will be uploaded on the system where tool then scans it, and recognizes issues such as, injuries and deformities, and sorts them into groups. It can also facilitate detection whether it is a certain region and it is a minor or major fracture. This will save doctors time and will facilitate their enhancement in the precision of diagnosis as well as the speed-up treatment processes. The key goal is to make the healthcare sector more productive by enabling the doctors to spend the majority of their time with the patients while the system assists the initial analysis.