The early diagnosis of bone cancer plays a vital role in enhancing patient treatment and recovery rates due to deep learning in recent years, which have demonstrated great potential in the automatic detection of bone cancer using medical imaging. The proposed research paper explores the use of InceptionV3 model, which is a robust deep learning convolutional neural network, and in the early stages of bone cancer detection. The study starts by stating the significance of early diagnosis and provides a simplified description of deep learning and how it can be used to process complex information about images. The reason behind the choice of InceptionV3 is the fact that it has advanced architecture that allows retrieval of vital features in medical images with very high precision. The model is trained and tested with a set of bone cancer images, and the experimental outcomes prove that the model has a high per-performance in terms of classifying and identifying cancerous regions. In general, the results confirm that InceptionV3 is useful to help healthcare professionals in the early diagnosis of the condition, which has the potential to implement interventions in time and provide improved patient care.