<p>Ferromagnetic continuum robots (FCRs) are highly promising for medical applications due to their flexibility and infinite degrees of freedom. However, their inherent nonlinear and magnetic properties lead to significant challenges in developing controllers that can ensure both real-time performance and precise motion control. These challenges become even more critical in high-risk medical scenarios, such as minimally invasive surgeries and delicate interventions, where precision and reliability are essential. To address these issues, this paper presents an innovative hybrid control approach that combines neural network-based modeling with feedback linearization, effectively mitigating the complex nonlinearities of FCRs. To improve both real-time implementation and accuracy, which are often compromised in conventional techniques, the proposed method applies feedback linearization to systematically eliminate system nonlinearities. This leads to a linear formulation of the dynamics, enabling simpler analysis and control design. This approach ensures precise trajectory tracking while maintaining system stability, which is essential for medical applications. The control strategy is implemented in two phases: initially, a neural network is trained using kinematic data to develop an inverse model that associates desired positions with the corresponding strains. In the second phase, a state feedback linearization controller is applied to regulate the robot’s motion with high precision, achieving accurate tip positioning and real-time trajectory tracking. The effectiveness of the proposed strategy is demonstrated through simulations, where its high accuracy and reliability are confirmed. By combining model-based feedback linearization with neural network-assisted inverse kinematics, this hybrid approach ensures real-time precision and enhances motion stability and adaptability, with position errors consistently remaining below 3.5% of the robot’s length.</p>