Osteosarcoma is one kind of cancer that starts in the cells that form bones. Most often, it is found in the long bones of the leg and sometimes in the arms. But bone tumors form in any bone in the human body. When cells grow and divide abnormally and uncontrollably, then they can form a mass or lump of tissue. Here, we have focused on the various applications of neural network architectures for the classification and detection of malignant bone tumors. For histological dataset analysis, YOLOv5 stands out with the highest accuracy (98.00%), indicating its superior performance in handling complex image classification tasks. The hybrid models ResNet50 and Inception V3, and MobileNet also demonstrate high accuracy (87.67% and 86.76%, respectively), with MobileNet being particularly notable for its efficiency in resource-constrained environments. Also, the X-ray image dataset YOLOv7, adapted for bone tumor detection, achieves a commendable accuracy of 71.65%. The high accuracy achieved by the various neural network architectures underscores their potential to assist healthcare professionals.