The evolution of orthopedic surgical practice, driven by advances in imaging techniques, has led to the creation of 3D biomodels that are increasingly being utilized in surgical centers. These biomodels, precise representations of human anatomy, have positively impacted orthopedic and traumatic treatment, allowing for a more precise approach in surgical planning and the manufacturing of customized tools. In the context of femoral head fractures, severe injuries affecting mobility and quality of life, treatment varies according to the type and severity of the fracture, ranging from conservative measures to complex surgical interventions, such as joint replacement with prostheses. However, in 3D surgical planning, the crucial step of fracture reduction can benefit from the use of the Random Sample Consensus (RANSAC) artificial intelligence algorithm. This method, widely employed in the registration and alignment of three-dimensional point clouds, offers a more objective and precise approach to aligning surfaces. This study aimed to simulate a femoral head fracture in a biomodel and apply the RANSAC method, using the healthy contralateral biomodel as a target. After simulating the fracture, sampling, and preprocessing steps, the RANSAC algorithm was applied and presented a new biomodel with the fracture reduced, with an RMS of 0.954, a value considered satisfactory for good alignment between two point clouds. This approach demonstrates the potential of integrating 3D technology and artificial intelligence in improving orthopedic surgical outcomes.