Human bones serve as protective structures for vital organs, and fractures represent either complete or partial breaks in these bones. This study aims to classify bone fractures to enhance surgical diagnosis and treatment. It specifically addresses five types of fractures: comminuted, fracture dislocations, oblique, pathological, and spiral fractures. While also considering multi-region fractures. The classification utilizes X-ray images, which are commonly used in medical settings for diagnosing fractures. To accomplish this, we used a hybrid approach that integrates various image processing techniques and machine learning methods. This includes using k-means and watershed algorithms for segmentation, which effectively isolates the fracture areas from complex background images. For feature extraction, we applied Convolutional Neural Networks (CNNs), enabling the automatic identification of relevant features from the segmented images. We then used Support Vector Machine (SVM) classification to accurately categorize the different types of fractures based on these extracted features. We thoroughly tested the proposed method and achieved an accuracy of 95%. This high accuracy shows that combining advanced image processing techniques with machine learning can greatly improve diagnosis in orthopedic settings, by helping doctors make faster and more accurate treatment decisions for patients with bone fractures; this study improves the reliability of fracture classification in clinical practice. Keywords: Bone Fractures, Image Processing, CNN and SVM.